Overview

Dataset statistics

Number of variables96
Number of observations9949
Missing cells89532
Missing cells (%)9.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory7.3 MiB
Average record size in memory768.0 B

Variable types

Numeric18
Categorical78

Alerts

idade12 has constant value "1.0"Constant
id2 is highly overall correlated with regiaoHigh correlation
idade6 is highly overall correlated with idade7 and 8 other fieldsHigh correlation
idade7 is highly overall correlated with idade6 and 16 other fieldsHigh correlation
idade8 is highly overall correlated with qp5 and 13 other fieldsHigh correlation
idade9 is highly overall correlated with idade7 and 15 other fieldsHigh correlation
idade10 is highly overall correlated with idade9 and 26 other fieldsHigh correlation
idade11 is highly overall correlated with q14 and 30 other fieldsHigh correlation
q3 is highly overall correlated with q4 and 14 other fieldsHigh correlation
q4 is highly overall correlated with q3 and 14 other fieldsHigh correlation
q13 is highly overall correlated with q17 and 35 other fieldsHigh correlation
q14 is highly overall correlated with idade11 and 13 other fieldsHigh correlation
q16 is highly overall correlated with idade11 and 15 other fieldsHigh correlation
q17 is highly overall correlated with q13 and 35 other fieldsHigh correlation
qp1 is highly overall correlated with idade11 and 12 other fieldsHigh correlation
regiao is highly overall correlated with id2 and 1 other fieldsHigh correlation
q1 is highly overall correlated with q3 and 40 other fieldsHigh correlation
q2 is highly overall correlated with idade10 and 20 other fieldsHigh correlation
q7 is highly overall correlated with idade7 and 18 other fieldsHigh correlation
q8 is highly overall correlated with idade10 and 19 other fieldsHigh correlation
q9 is highly overall correlated with q3 and 17 other fieldsHigh correlation
q10 is highly overall correlated with q3 and 17 other fieldsHigh correlation
q11 is highly overall correlated with idade10 and 22 other fieldsHigh correlation
q12 is highly overall correlated with q3 and 39 other fieldsHigh correlation
q15 is highly overall correlated with idade11 and 3 other fieldsHigh correlation
q18 is highly overall correlated with q3 and 17 other fieldsHigh correlation
q19 is highly overall correlated with idade11 and 18 other fieldsHigh correlation
q20 is highly overall correlated with q3 and 17 other fieldsHigh correlation
q21 is highly overall correlated with q3 and 17 other fieldsHigh correlation
qp2 is highly overall correlated with q13 and 7 other fieldsHigh correlation
qp3 is highly overall correlated with q3 and 39 other fieldsHigh correlation
qp4 is highly overall correlated with q13 and 30 other fieldsHigh correlation
qp5 is highly overall correlated with idade6 and 9 other fieldsHigh correlation
qp6 is highly overall correlated with qp4 and 9 other fieldsHigh correlation
qp7 is highly overall correlated with q13 and 32 other fieldsHigh correlation
qp8 is highly overall correlated with idade7 and 15 other fieldsHigh correlation
qp9 is highly overall correlated with qp4 and 30 other fieldsHigh correlation
qp10 is highly overall correlated with q13 and 30 other fieldsHigh correlation
qp11 is highly overall correlated with idade6 and 15 other fieldsHigh correlation
qp12 is highly overall correlated with qp4 and 30 other fieldsHigh correlation
qp13 is highly overall correlated with q13 and 34 other fieldsHigh correlation
qp14 is highly overall correlated with idade7 and 17 other fieldsHigh correlation
qp15 is highly overall correlated with qp6 and 15 other fieldsHigh correlation
qp16 is highly overall correlated with q13 and 33 other fieldsHigh correlation
qp17 is highly overall correlated with qp11 and 11 other fieldsHigh correlation
qp18 is highly overall correlated with idade10 and 32 other fieldsHigh correlation
qp19 is highly overall correlated with q13 and 35 other fieldsHigh correlation
qp20 is highly overall correlated with idade6 and 24 other fieldsHigh correlation
qp21 is highly overall correlated with idade10 and 18 other fieldsHigh correlation
qp22 is highly overall correlated with q13 and 40 other fieldsHigh correlation
qp23 is highly overall correlated with idade6 and 27 other fieldsHigh correlation
qp24 is highly overall correlated with idade10 and 18 other fieldsHigh correlation
qp25 is highly overall correlated with q13 and 38 other fieldsHigh correlation
qp26 is highly overall correlated with idade7 and 23 other fieldsHigh correlation
qp27 is highly overall correlated with qp6 and 15 other fieldsHigh correlation
qp28 is highly overall correlated with q13 and 36 other fieldsHigh correlation
qp29 is highly overall correlated with idade7 and 23 other fieldsHigh correlation
qp30 is highly overall correlated with idade10 and 16 other fieldsHigh correlation
qp31 is highly overall correlated with q13 and 37 other fieldsHigh correlation
qp32 is highly overall correlated with idade6 and 23 other fieldsHigh correlation
qp33 is highly overall correlated with qp9 and 18 other fieldsHigh correlation
qp34 is highly overall correlated with q13 and 34 other fieldsHigh correlation
qp35 is highly overall correlated with idade7 and 20 other fieldsHigh correlation
qp36 is highly overall correlated with qp4 and 30 other fieldsHigh correlation
qp37 is highly overall correlated with q13 and 35 other fieldsHigh correlation
qp38 is highly overall correlated with idade6 and 13 other fieldsHigh correlation
qp39 is highly overall correlated with idade10 and 18 other fieldsHigh correlation
qp40 is highly overall correlated with q13 and 33 other fieldsHigh correlation
qp41 is highly overall correlated with qp23 and 11 other fieldsHigh correlation
qp42 is highly overall correlated with idade10 and 18 other fieldsHigh correlation
qp43 is highly overall correlated with q13 and 34 other fieldsHigh correlation
qp44 is highly overall correlated with idade7 and 7 other fieldsHigh correlation
qp45 is highly overall correlated with idade10 and 26 other fieldsHigh correlation
qp46 is highly overall correlated with q13 and 34 other fieldsHigh correlation
qp47 is highly overall correlated with idade6 and 16 other fieldsHigh correlation
qp48 is highly overall correlated with idade10 and 18 other fieldsHigh correlation
qp49 is highly overall correlated with q13 and 35 other fieldsHigh correlation
qp50 is highly overall correlated with idade6 and 17 other fieldsHigh correlation
qp51 is highly overall correlated with idade10 and 16 other fieldsHigh correlation
qp52 is highly overall correlated with q13 and 24 other fieldsHigh correlation
qp53 is highly overall correlated with q13 and 23 other fieldsHigh correlation
qp54 is highly overall correlated with q13 and 23 other fieldsHigh correlation
qp55 is highly overall correlated with q13 and 23 other fieldsHigh correlation
r1 is highly overall correlated with r2 and 7 other fieldsHigh correlation
r2 is highly overall correlated with r1 and 7 other fieldsHigh correlation
r3 is highly overall correlated with r1 and 7 other fieldsHigh correlation
r4 is highly overall correlated with r1 and 7 other fieldsHigh correlation
r5 is highly overall correlated with r1 and 7 other fieldsHigh correlation
r6 is highly overall correlated with r1 and 7 other fieldsHigh correlation
r7 is highly overall correlated with r1 and 7 other fieldsHigh correlation
r8 is highly overall correlated with r1 and 7 other fieldsHigh correlation
r9 is highly overall correlated with r1 and 7 other fieldsHigh correlation
q8 is highly imbalanced (54.4%)Imbalance
q10 is highly imbalanced (51.0%)Imbalance
q12 is highly imbalanced (59.5%)Imbalance
q18 is highly imbalanced (65.3%)Imbalance
qp2 is highly imbalanced (70.1%)Imbalance
qp3 is highly imbalanced (58.7%)Imbalance
qp4 is highly imbalanced (79.5%)Imbalance
qp5 is highly imbalanced (97.4%)Imbalance
qp6 is highly imbalanced (90.9%)Imbalance
qp7 is highly imbalanced (79.6%)Imbalance
qp8 is highly imbalanced (97.5%)Imbalance
qp9 is highly imbalanced (89.2%)Imbalance
qp10 is highly imbalanced (79.6%)Imbalance
qp11 is highly imbalanced (97.5%)Imbalance
qp12 is highly imbalanced (89.7%)Imbalance
qp13 is highly imbalanced (79.5%)Imbalance
qp14 is highly imbalanced (97.3%)Imbalance
qp15 is highly imbalanced (92.3%)Imbalance
qp16 is highly imbalanced (79.3%)Imbalance
qp17 is highly imbalanced (97.0%)Imbalance
qp18 is highly imbalanced (90.0%)Imbalance
qp19 is highly imbalanced (79.3%)Imbalance
qp20 is highly imbalanced (97.4%)Imbalance
qp21 is highly imbalanced (90.9%)Imbalance
qp22 is highly imbalanced (79.4%)Imbalance
qp23 is highly imbalanced (97.6%)Imbalance
qp24 is highly imbalanced (90.9%)Imbalance
qp25 is highly imbalanced (79.7%)Imbalance
qp26 is highly imbalanced (97.6%)Imbalance
qp27 is highly imbalanced (93.2%)Imbalance
qp28 is highly imbalanced (79.4%)Imbalance
qp29 is highly imbalanced (97.7%)Imbalance
qp30 is highly imbalanced (92.4%)Imbalance
qp31 is highly imbalanced (79.4%)Imbalance
qp32 is highly imbalanced (97.6%)Imbalance
qp33 is highly imbalanced (91.8%)Imbalance
qp34 is highly imbalanced (79.5%)Imbalance
qp35 is highly imbalanced (97.8%)Imbalance
qp36 is highly imbalanced (90.3%)Imbalance
qp37 is highly imbalanced (79.4%)Imbalance
qp38 is highly imbalanced (97.8%)Imbalance
qp39 is highly imbalanced (92.7%)Imbalance
qp40 is highly imbalanced (79.4%)Imbalance
qp41 is highly imbalanced (97.0%)Imbalance
qp42 is highly imbalanced (92.8%)Imbalance
qp43 is highly imbalanced (79.4%)Imbalance
qp44 is highly imbalanced (97.6%)Imbalance
qp45 is highly imbalanced (92.6%)Imbalance
qp46 is highly imbalanced (79.4%)Imbalance
qp47 is highly imbalanced (97.9%)Imbalance
qp48 is highly imbalanced (92.2%)Imbalance
qp49 is highly imbalanced (79.7%)Imbalance
qp50 is highly imbalanced (97.5%)Imbalance
qp51 is highly imbalanced (93.5%)Imbalance
qp52 is highly imbalanced (78.2%)Imbalance
qp53 is highly imbalanced (78.1%)Imbalance
qp54 is highly imbalanced (76.8%)Imbalance
qp55 is highly imbalanced (78.2%)Imbalance
r1 is highly imbalanced (53.1%)Imbalance
idade2 has 2734 (27.5%) missing valuesMissing
idade4 has 8376 (84.2%) missing valuesMissing
idade5 has 9297 (93.4%) missing valuesMissing
idade6 has 9679 (97.3%) missing valuesMissing
idade7 has 9821 (98.7%) missing valuesMissing
idade8 has 9894 (99.4%) missing valuesMissing
idade9 has 9919 (99.7%) missing valuesMissing
idade10 has 9933 (99.8%) missing valuesMissing
idade11 has 9935 (99.9%) missing valuesMissing
idade12 has 9944 (99.9%) missing valuesMissing
id2 is uniformly distributedUniform
id2 has unique valuesUnique
q13 has 401 (4.0%) zerosZeros
q16 has 4957 (49.8%) zerosZeros
q17 has 1017 (10.2%) zerosZeros

Reproduction

Analysis started2023-03-23 13:32:00.086878
Analysis finished2023-03-23 13:34:14.183317
Duration2 minutes and 14.1 seconds
Software versionpandas-profiling v3.6.6
Download configurationconfig.json

Variables

id2
Real number (ℝ)

HIGH CORRELATION  UNIFORM  UNIQUE 

Distinct9949
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20204975
Minimum20200001
Maximum20209949
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size77.9 KiB
2023-03-23T10:34:14.550078image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum20200001
5-th percentile20200498
Q120202488
median20204975
Q320207462
95-th percentile20209452
Maximum20209949
Range9948
Interquartile range (IQR)4974

Descriptive statistics

Standard deviation2872.1732
Coefficient of variation (CV)0.00014215178
Kurtosis-1.2
Mean20204975
Median Absolute Deviation (MAD)2487
Skewness0
Sum2.010193 × 1011
Variance8249379.2
MonotonicityStrictly increasing
2023-03-23T10:34:14.756526image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20200001 1
 
< 0.1%
20206637 1
 
< 0.1%
20206630 1
 
< 0.1%
20206631 1
 
< 0.1%
20206632 1
 
< 0.1%
20206633 1
 
< 0.1%
20206634 1
 
< 0.1%
20206635 1
 
< 0.1%
20206636 1
 
< 0.1%
20206638 1
 
< 0.1%
Other values (9939) 9939
99.9%
ValueCountFrequency (%)
20200001 1
< 0.1%
20200002 1
< 0.1%
20200003 1
< 0.1%
20200004 1
< 0.1%
20200005 1
< 0.1%
20200006 1
< 0.1%
20200007 1
< 0.1%
20200008 1
< 0.1%
20200009 1
< 0.1%
20200010 1
< 0.1%
ValueCountFrequency (%)
20209949 1
< 0.1%
20209948 1
< 0.1%
20209947 1
< 0.1%
20209946 1
< 0.1%
20209945 1
< 0.1%
20209944 1
< 0.1%
20209943 1
< 0.1%
20209942 1
< 0.1%
20209941 1
< 0.1%
20209940 1
< 0.1%

regiao
Categorical

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size77.9 KiB
3
4114 
2
2707 
4
1336 
5
1053 
1
739 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters9949
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
3 4114
41.4%
2 2707
27.2%
4 1336
 
13.4%
5 1053
 
10.6%
1 739
 
7.4%

Length

2023-03-23T10:34:14.917095image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-23T10:34:15.072679image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
3 4114
41.4%
2 2707
27.2%
4 1336
 
13.4%
5 1053
 
10.6%
1 739
 
7.4%

Most occurring characters

ValueCountFrequency (%)
3 4114
41.4%
2 2707
27.2%
4 1336
 
13.4%
5 1053
 
10.6%
1 739
 
7.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 9949
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 4114
41.4%
2 2707
27.2%
4 1336
 
13.4%
5 1053
 
10.6%
1 739
 
7.4%

Most occurring scripts

ValueCountFrequency (%)
Common 9949
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3 4114
41.4%
2 2707
27.2%
4 1336
 
13.4%
5 1053
 
10.6%
1 739
 
7.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9949
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 4114
41.4%
2 2707
27.2%
4 1336
 
13.4%
5 1053
 
10.6%
1 739
 
7.4%

idade2
Real number (ℝ)

Distinct101
Distinct (%)1.4%
Missing2734
Missing (%)27.5%
Infinite0
Infinite (%)0.0%
Mean57.448371
Minimum0
Maximum119
Zeros14
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size77.9 KiB
2023-03-23T10:34:15.263170image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile24
Q150
median59
Q368
95-th percentile83
Maximum119
Range119
Interquartile range (IQR)18

Descriptive statistics

Standard deviation16.989888
Coefficient of variation (CV)0.29574186
Kurtosis0.3315413
Mean57.448371
Median Absolute Deviation (MAD)9
Skewness-0.53686017
Sum414490
Variance288.6563
MonotonicityNot monotonic
2023-03-23T10:34:15.434711image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
59 244
 
2.5%
58 223
 
2.2%
61 220
 
2.2%
54 216
 
2.2%
62 214
 
2.2%
56 214
 
2.2%
63 201
 
2.0%
65 198
 
2.0%
53 195
 
2.0%
60 191
 
1.9%
Other values (91) 5099
51.3%
(Missing) 2734
27.5%
ValueCountFrequency (%)
0 14
0.1%
2 1
 
< 0.1%
3 3
 
< 0.1%
5 5
 
0.1%
6 3
 
< 0.1%
7 4
 
< 0.1%
8 5
 
0.1%
9 5
 
0.1%
10 7
0.1%
11 5
 
0.1%
ValueCountFrequency (%)
119 1
 
< 0.1%
101 2
 
< 0.1%
100 2
 
< 0.1%
99 6
 
0.1%
98 5
 
0.1%
97 2
 
< 0.1%
96 4
 
< 0.1%
95 14
0.1%
94 17
0.2%
93 9
0.1%

idade4
Real number (ℝ)

Distinct87
Distinct (%)5.5%
Missing8376
Missing (%)84.2%
Infinite0
Infinite (%)0.0%
Mean25.409409
Minimum0
Maximum95
Zeros18
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size77.9 KiB
2023-03-23T10:34:15.742893image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3.6
Q114
median21
Q332
95-th percentile59
Maximum95
Range95
Interquartile range (IQR)18

Descriptive statistics

Standard deviation17.583649
Coefficient of variation (CV)0.69201329
Kurtosis2.0716563
Mean25.409409
Median Absolute Deviation (MAD)9
Skewness1.341612
Sum39969
Variance309.1847
MonotonicityNot monotonic
2023-03-23T10:34:16.174761image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
18 67
 
0.7%
20 65
 
0.7%
13 50
 
0.5%
16 50
 
0.5%
19 49
 
0.5%
14 47
 
0.5%
24 47
 
0.5%
21 47
 
0.5%
25 43
 
0.4%
22 42
 
0.4%
Other values (77) 1066
 
10.7%
(Missing) 8376
84.2%
ValueCountFrequency (%)
0 18
0.2%
1 22
0.2%
2 26
0.3%
3 13
0.1%
4 17
0.2%
5 28
0.3%
6 18
0.2%
7 29
0.3%
8 28
0.3%
9 25
0.3%
ValueCountFrequency (%)
95 1
 
< 0.1%
94 1
 
< 0.1%
93 4
< 0.1%
89 4
< 0.1%
88 1
 
< 0.1%
86 1
 
< 0.1%
85 2
 
< 0.1%
84 6
0.1%
83 5
0.1%
82 4
< 0.1%

idade5
Real number (ℝ)

Distinct76
Distinct (%)11.7%
Missing9297
Missing (%)93.4%
Infinite0
Infinite (%)0.0%
Mean21.42638
Minimum0
Maximum92
Zeros14
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size77.9 KiB
2023-03-23T10:34:16.634519image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q110
median16
Q327
95-th percentile60
Maximum92
Range92
Interquartile range (IQR)17

Descriptive statistics

Standard deviation17.740815
Coefficient of variation (CV)0.82798935
Kurtosis2.8965129
Mean21.42638
Median Absolute Deviation (MAD)8
Skewness1.641096
Sum13970
Variance314.73651
MonotonicityNot monotonic
2023-03-23T10:34:17.111246image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12 33
 
0.3%
16 30
 
0.3%
14 27
 
0.3%
8 24
 
0.2%
10 23
 
0.2%
26 22
 
0.2%
15 22
 
0.2%
13 21
 
0.2%
7 21
 
0.2%
5 20
 
0.2%
Other values (66) 409
 
4.1%
(Missing) 9297
93.4%
ValueCountFrequency (%)
0 14
0.1%
1 11
0.1%
2 15
0.2%
3 14
0.1%
4 12
0.1%
5 20
0.2%
6 10
0.1%
7 21
0.2%
8 24
0.2%
9 19
0.2%
ValueCountFrequency (%)
92 1
< 0.1%
91 1
< 0.1%
90 1
< 0.1%
89 1
< 0.1%
88 1
< 0.1%
87 1
< 0.1%
85 1
< 0.1%
84 2
< 0.1%
83 1
< 0.1%
82 1
< 0.1%

idade6
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct53
Distinct (%)19.6%
Missing9679
Missing (%)97.3%
Infinite0
Infinite (%)0.0%
Mean19.366667
Minimum0
Maximum95
Zeros9
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size77.9 KiB
2023-03-23T10:34:17.518157image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q18
median15
Q325
95-th percentile55
Maximum95
Range95
Interquartile range (IQR)17

Descriptive statistics

Standard deviation16.438315
Coefficient of variation (CV)0.84879426
Kurtosis3.7099131
Mean19.366667
Median Absolute Deviation (MAD)8.5
Skewness1.6767671
Sum5229
Variance270.21822
MonotonicityNot monotonic
2023-03-23T10:34:17.709644image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8 15
 
0.2%
5 13
 
0.1%
10 12
 
0.1%
12 10
 
0.1%
6 10
 
0.1%
18 9
 
0.1%
4 9
 
0.1%
0 9
 
0.1%
22 9
 
0.1%
23 9
 
0.1%
Other values (43) 165
 
1.7%
(Missing) 9679
97.3%
ValueCountFrequency (%)
0 9
0.1%
1 7
0.1%
2 3
 
< 0.1%
3 4
 
< 0.1%
4 9
0.1%
5 13
0.1%
6 10
0.1%
7 8
0.1%
8 15
0.2%
9 6
 
0.1%
ValueCountFrequency (%)
95 2
 
< 0.1%
84 1
 
< 0.1%
68 2
 
< 0.1%
62 1
 
< 0.1%
60 1
 
< 0.1%
58 3
< 0.1%
56 1
 
< 0.1%
55 4
< 0.1%
52 5
0.1%
45 1
 
< 0.1%

idade7
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct36
Distinct (%)28.1%
Missing9821
Missing (%)98.7%
Infinite0
Infinite (%)0.0%
Mean16.1875
Minimum0
Maximum52
Zeros3
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size77.9 KiB
2023-03-23T10:34:17.883179image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1.35
Q16
median12
Q323
95-th percentile44.9
Maximum52
Range52
Interquartile range (IQR)17

Descriptive statistics

Standard deviation13.425648
Coefficient of variation (CV)0.82938368
Kurtosis0.0061093636
Mean16.1875
Median Absolute Deviation (MAD)8
Skewness0.96104712
Sum2072
Variance180.24803
MonotonicityNot monotonic
2023-03-23T10:34:18.059708image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
4 8
 
0.1%
6 8
 
0.1%
2 7
 
0.1%
17 7
 
0.1%
7 7
 
0.1%
32 6
 
0.1%
10 5
 
0.1%
8 5
 
0.1%
18 5
 
0.1%
1 4
 
< 0.1%
Other values (26) 66
 
0.7%
(Missing) 9821
98.7%
ValueCountFrequency (%)
0 3
 
< 0.1%
1 4
< 0.1%
2 7
0.1%
3 4
< 0.1%
4 8
0.1%
5 4
< 0.1%
6 8
0.1%
7 7
0.1%
8 5
0.1%
9 4
< 0.1%
ValueCountFrequency (%)
52 1
 
< 0.1%
50 2
 
< 0.1%
47 4
< 0.1%
41 3
< 0.1%
39 2
 
< 0.1%
37 1
 
< 0.1%
36 2
 
< 0.1%
35 2
 
< 0.1%
33 1
 
< 0.1%
32 6
0.1%

idade8
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct22
Distinct (%)40.0%
Missing9894
Missing (%)99.4%
Infinite0
Infinite (%)0.0%
Mean15.945455
Minimum0
Maximum70
Zeros7
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size77.9 KiB
2023-03-23T10:34:18.209307image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median11
Q319.5
95-th percentile48
Maximum70
Range70
Interquartile range (IQR)17.5

Descriptive statistics

Standard deviation17.780991
Coefficient of variation (CV)1.1151135
Kurtosis1.2508128
Mean15.945455
Median Absolute Deviation (MAD)9
Skewness1.3965836
Sum877
Variance316.16364
MonotonicityNot monotonic
2023-03-23T10:34:18.341953image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
0 7
 
0.1%
13 6
 
0.1%
19 5
 
0.1%
45 4
 
< 0.1%
1 4
 
< 0.1%
2 4
 
< 0.1%
4 3
 
< 0.1%
3 3
 
< 0.1%
11 3
 
< 0.1%
48 3
 
< 0.1%
Other values (12) 13
 
0.1%
(Missing) 9894
99.4%
ValueCountFrequency (%)
0 7
0.1%
1 4
< 0.1%
2 4
< 0.1%
3 3
< 0.1%
4 3
< 0.1%
5 1
 
< 0.1%
6 1
 
< 0.1%
7 1
 
< 0.1%
8 1
 
< 0.1%
11 3
< 0.1%
ValueCountFrequency (%)
70 1
 
< 0.1%
65 1
 
< 0.1%
48 3
< 0.1%
45 4
< 0.1%
29 1
 
< 0.1%
25 1
 
< 0.1%
23 2
 
< 0.1%
20 1
 
< 0.1%
19 5
0.1%
17 1
 
< 0.1%

idade9
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct15
Distinct (%)50.0%
Missing9919
Missing (%)99.7%
Infinite0
Infinite (%)0.0%
Mean20.2
Minimum0
Maximum72
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size77.9 KiB
2023-03-23T10:34:18.482577image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q14.25
median16
Q322
95-th percentile72
Maximum72
Range72
Interquartile range (IQR)17.75

Descriptive statistics

Standard deviation22.256189
Coefficient of variation (CV)1.1017915
Kurtosis1.8189277
Mean20.2
Median Absolute Deviation (MAD)11
Skewness1.6679499
Sum606
Variance495.33793
MonotonicityNot monotonic
2023-03-23T10:34:18.685041image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
22 5
 
0.1%
3 4
 
< 0.1%
72 4
 
< 0.1%
16 3
 
< 0.1%
1 2
 
< 0.1%
5 2
 
< 0.1%
27 2
 
< 0.1%
8 1
 
< 0.1%
4 1
 
< 0.1%
7 1
 
< 0.1%
Other values (5) 5
 
0.1%
(Missing) 9919
99.7%
ValueCountFrequency (%)
0 1
 
< 0.1%
1 2
< 0.1%
3 4
< 0.1%
4 1
 
< 0.1%
5 2
< 0.1%
7 1
 
< 0.1%
8 1
 
< 0.1%
13 1
 
< 0.1%
15 1
 
< 0.1%
16 3
< 0.1%
ValueCountFrequency (%)
72 4
< 0.1%
27 2
 
< 0.1%
22 5
0.1%
18 1
 
< 0.1%
17 1
 
< 0.1%
16 3
< 0.1%
15 1
 
< 0.1%
13 1
 
< 0.1%
8 1
 
< 0.1%
7 1
 
< 0.1%

idade10
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct8
Distinct (%)50.0%
Missing9933
Missing (%)99.8%
Infinite0
Infinite (%)0.0%
Mean19.0625
Minimum2
Maximum50
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size77.9 KiB
2023-03-23T10:34:18.992217image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile2
Q14
median23
Q324
95-th percentile41.75
Maximum50
Range48
Interquartile range (IQR)20

Descriptive statistics

Standard deviation13.824947
Coefficient of variation (CV)0.72524313
Kurtosis0.16227128
Mean19.0625
Median Absolute Deviation (MAD)6
Skewness0.49178276
Sum305
Variance191.12917
MonotonicityNot monotonic
2023-03-23T10:34:19.281443image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
24 5
 
0.1%
23 3
 
< 0.1%
4 2
 
< 0.1%
2 2
 
< 0.1%
50 1
 
< 0.1%
3 1
 
< 0.1%
39 1
 
< 0.1%
12 1
 
< 0.1%
(Missing) 9933
99.8%
ValueCountFrequency (%)
2 2
 
< 0.1%
3 1
 
< 0.1%
4 2
 
< 0.1%
12 1
 
< 0.1%
23 3
< 0.1%
24 5
0.1%
39 1
 
< 0.1%
50 1
 
< 0.1%
ValueCountFrequency (%)
50 1
 
< 0.1%
39 1
 
< 0.1%
24 5
0.1%
23 3
< 0.1%
12 1
 
< 0.1%
4 2
 
< 0.1%
3 1
 
< 0.1%
2 2
 
< 0.1%

idade11
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct6
Distinct (%)42.9%
Missing9935
Missing (%)99.9%
Infinite0
Infinite (%)0.0%
Mean6.4285714
Minimum1
Maximum14
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size77.9 KiB
2023-03-23T10:34:19.538755image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12.5
median7
Q38
95-th percentile12.05
Maximum14
Range13
Interquartile range (IQR)5.5

Descriptive statistics

Standard deviation4.070802
Coefficient of variation (CV)0.63323586
Kurtosis-0.55555221
Mean6.4285714
Median Absolute Deviation (MAD)2
Skewness-0.074607788
Sum90
Variance16.571429
MonotonicityNot monotonic
2023-03-23T10:34:19.820007image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
7 5
 
0.1%
1 4
 
< 0.1%
8 2
 
< 0.1%
14 1
 
< 0.1%
11 1
 
< 0.1%
10 1
 
< 0.1%
(Missing) 9935
99.9%
ValueCountFrequency (%)
1 4
< 0.1%
7 5
0.1%
8 2
 
< 0.1%
10 1
 
< 0.1%
11 1
 
< 0.1%
14 1
 
< 0.1%
ValueCountFrequency (%)
14 1
 
< 0.1%
11 1
 
< 0.1%
10 1
 
< 0.1%
8 2
 
< 0.1%
7 5
0.1%
1 4
< 0.1%

idade12
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)20.0%
Missing9944
Missing (%)99.9%
Memory size77.9 KiB
1.0

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters15
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 5
 
0.1%
(Missing) 9944
99.9%

Length

2023-03-23T10:34:20.170072image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-23T10:34:20.477266image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0 5
100.0%

Most occurring characters

ValueCountFrequency (%)
1 5
33.3%
. 5
33.3%
0 5
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 10
66.7%
Other Punctuation 5
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 5
50.0%
0 5
50.0%
Other Punctuation
ValueCountFrequency (%)
. 5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 15
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 5
33.3%
. 5
33.3%
0 5
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 5
33.3%
. 5
33.3%
0 5
33.3%

idade
Real number (ℝ)

Distinct54
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean66.336215
Minimum50
Maximum109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size77.9 KiB
2023-03-23T10:34:20.645793image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum50
5-th percentile53
Q158
median65
Q373
95-th percentile85
Maximum109
Range59
Interquartile range (IQR)15

Descriptive statistics

Standard deviation10.053518
Coefficient of variation (CV)0.15155398
Kurtosis-0.2501293
Mean66.336215
Median Absolute Deviation (MAD)7
Skewness0.58507341
Sum659979
Variance101.07322
MonotonicityNot monotonic
2023-03-23T10:34:20.847256image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
57 435
 
4.4%
58 402
 
4.0%
63 396
 
4.0%
55 387
 
3.9%
56 386
 
3.9%
54 383
 
3.8%
62 377
 
3.8%
59 371
 
3.7%
60 361
 
3.6%
61 354
 
3.6%
Other values (44) 6097
61.3%
ValueCountFrequency (%)
50 150
 
1.5%
51 106
 
1.1%
52 147
 
1.5%
53 253
2.5%
54 383
3.8%
55 387
3.9%
56 386
3.9%
57 435
4.4%
58 402
4.0%
59 371
3.7%
ValueCountFrequency (%)
109 2
 
< 0.1%
108 1
 
< 0.1%
101 2
 
< 0.1%
100 4
 
< 0.1%
99 6
 
0.1%
98 8
0.1%
97 4
 
< 0.1%
96 10
0.1%
95 13
0.1%
94 17
0.2%

q1
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size77.9 KiB
1
8729 
0
1220 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters9949
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 8729
87.7%
0 1220
 
12.3%

Length

2023-03-23T10:34:21.021789image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-23T10:34:21.167401image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
1 8729
87.7%
0 1220
 
12.3%

Most occurring characters

ValueCountFrequency (%)
1 8729
87.7%
0 1220
 
12.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 9949
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 8729
87.7%
0 1220
 
12.3%

Most occurring scripts

ValueCountFrequency (%)
Common 9949
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 8729
87.7%
0 1220
 
12.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9949
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 8729
87.7%
0 1220
 
12.3%

q2
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size77.9 KiB
1
6616 
0
2047 
8
1220 
9
 
66

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters9949
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
1 6616
66.5%
0 2047
 
20.6%
8 1220
 
12.3%
9 66
 
0.7%

Length

2023-03-23T10:34:21.286112image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-23T10:34:21.434684image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
1 6616
66.5%
0 2047
 
20.6%
8 1220
 
12.3%
9 66
 
0.7%

Most occurring characters

ValueCountFrequency (%)
1 6616
66.5%
0 2047
 
20.6%
8 1220
 
12.3%
9 66
 
0.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 9949
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 6616
66.5%
0 2047
 
20.6%
8 1220
 
12.3%
9 66
 
0.7%

Most occurring scripts

ValueCountFrequency (%)
Common 9949
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 6616
66.5%
0 2047
 
20.6%
8 1220
 
12.3%
9 66
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9949
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 6616
66.5%
0 2047
 
20.6%
8 1220
 
12.3%
9 66
 
0.7%

q3
Real number (ℝ)

Distinct8
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.9957785
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size77.9 KiB
2023-03-23T10:34:21.554367image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q13
median4
Q34
95-th percentile8
Maximum10
Range9
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.7903772
Coefficient of variation (CV)0.44806719
Kurtosis1.1130391
Mean3.9957785
Median Absolute Deviation (MAD)1
Skewness1.2823434
Sum39754
Variance3.2054506
MonotonicityNot monotonic
2023-03-23T10:34:21.662077image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
3 3942
39.6%
4 2728
27.4%
8 1220
 
12.3%
5 996
 
10.0%
2 711
 
7.1%
1 295
 
3.0%
10 46
 
0.5%
9 11
 
0.1%
ValueCountFrequency (%)
1 295
 
3.0%
2 711
 
7.1%
3 3942
39.6%
4 2728
27.4%
5 996
 
10.0%
8 1220
 
12.3%
9 11
 
0.1%
10 46
 
0.5%
ValueCountFrequency (%)
10 46
 
0.5%
9 11
 
0.1%
8 1220
 
12.3%
5 996
 
10.0%
4 2728
27.4%
3 3942
39.6%
2 711
 
7.1%
1 295
 
3.0%

q4
Real number (ℝ)

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.0711629
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size77.9 KiB
2023-03-23T10:34:21.772782image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q12
median2
Q33
95-th percentile8
Maximum10
Range9
Interquartile range (IQR)1

Descriptive statistics

Standard deviation2.0337328
Coefficient of variation (CV)0.66220284
Kurtosis2.283759
Mean3.0711629
Median Absolute Deviation (MAD)0
Skewness1.9350191
Sum30555
Variance4.1360692
MonotonicityNot monotonic
2023-03-23T10:34:21.886477image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
2 5323
53.5%
3 2969
29.8%
8 1220
 
12.3%
1 342
 
3.4%
9 50
 
0.5%
10 45
 
0.5%
ValueCountFrequency (%)
1 342
 
3.4%
2 5323
53.5%
3 2969
29.8%
8 1220
 
12.3%
9 50
 
0.5%
10 45
 
0.5%
ValueCountFrequency (%)
10 45
 
0.5%
9 50
 
0.5%
8 1220
 
12.3%
3 2969
29.8%
2 5323
53.5%
1 342
 
3.4%

q7
Categorical

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size77.9 KiB
1
6269 
0
1345 
8
1220 
9
902 
10
 
213

Length

Max length2
Median length1
Mean length1.0214092
Min length1

Characters and Unicode

Total characters10162
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row9

Common Values

ValueCountFrequency (%)
1 6269
63.0%
0 1345
 
13.5%
8 1220
 
12.3%
9 902
 
9.1%
10 213
 
2.1%

Length

2023-03-23T10:34:22.016131image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-23T10:34:22.169719image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
1 6269
63.0%
0 1345
 
13.5%
8 1220
 
12.3%
9 902
 
9.1%
10 213
 
2.1%

Most occurring characters

ValueCountFrequency (%)
1 6482
63.8%
0 1558
 
15.3%
8 1220
 
12.0%
9 902
 
8.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 10162
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 6482
63.8%
0 1558
 
15.3%
8 1220
 
12.0%
9 902
 
8.9%

Most occurring scripts

ValueCountFrequency (%)
Common 10162
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 6482
63.8%
0 1558
 
15.3%
8 1220
 
12.0%
9 902
 
8.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 10162
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 6482
63.8%
0 1558
 
15.3%
8 1220
 
12.0%
9 902
 
8.9%

q8
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size77.9 KiB
1
7901 
8
1220 
0
 
389
9
 
259
10
 
180

Length

Max length2
Median length1
Mean length1.0180923
Min length1

Characters and Unicode

Total characters10129
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row0
4th row1
5th row9

Common Values

ValueCountFrequency (%)
1 7901
79.4%
8 1220
 
12.3%
0 389
 
3.9%
9 259
 
2.6%
10 180
 
1.8%

Length

2023-03-23T10:34:22.305358image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-23T10:34:22.457950image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
1 7901
79.4%
8 1220
 
12.3%
0 389
 
3.9%
9 259
 
2.6%
10 180
 
1.8%

Most occurring characters

ValueCountFrequency (%)
1 8081
79.8%
8 1220
 
12.0%
0 569
 
5.6%
9 259
 
2.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 10129
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 8081
79.8%
8 1220
 
12.0%
0 569
 
5.6%
9 259
 
2.6%

Most occurring scripts

ValueCountFrequency (%)
Common 10129
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 8081
79.8%
8 1220
 
12.0%
0 569
 
5.6%
9 259
 
2.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 10129
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 8081
79.8%
8 1220
 
12.0%
0 569
 
5.6%
9 259
 
2.6%

q9
Categorical

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size77.9 KiB
1
6682 
8
1220 
0
1189 
9
 
639
10
 
219

Length

Max length2
Median length1
Mean length1.0220123
Min length1

Characters and Unicode

Total characters10168
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row9
4th row1
5th row9

Common Values

ValueCountFrequency (%)
1 6682
67.2%
8 1220
 
12.3%
0 1189
 
12.0%
9 639
 
6.4%
10 219
 
2.2%

Length

2023-03-23T10:34:22.628496image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-23T10:34:22.969586image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
1 6682
67.2%
8 1220
 
12.3%
0 1189
 
12.0%
9 639
 
6.4%
10 219
 
2.2%

Most occurring characters

ValueCountFrequency (%)
1 6901
67.9%
0 1408
 
13.8%
8 1220
 
12.0%
9 639
 
6.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 10168
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 6901
67.9%
0 1408
 
13.8%
8 1220
 
12.0%
9 639
 
6.3%

Most occurring scripts

ValueCountFrequency (%)
Common 10168
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 6901
67.9%
0 1408
 
13.8%
8 1220
 
12.0%
9 639
 
6.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 10168
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 6901
67.9%
0 1408
 
13.8%
8 1220
 
12.0%
9 639
 
6.3%

q10
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size77.9 KiB
1
7619 
8
1220 
0
832 
9
 
157
10
 
121

Length

Max length2
Median length1
Mean length1.012162
Min length1

Characters and Unicode

Total characters10070
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row9

Common Values

ValueCountFrequency (%)
1 7619
76.6%
8 1220
 
12.3%
0 832
 
8.4%
9 157
 
1.6%
10 121
 
1.2%

Length

2023-03-23T10:34:23.262804image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-23T10:34:23.611870image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
1 7619
76.6%
8 1220
 
12.3%
0 832
 
8.4%
9 157
 
1.6%
10 121
 
1.2%

Most occurring characters

ValueCountFrequency (%)
1 7740
76.9%
8 1220
 
12.1%
0 953
 
9.5%
9 157
 
1.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 10070
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 7740
76.9%
8 1220
 
12.1%
0 953
 
9.5%
9 157
 
1.6%

Most occurring scripts

ValueCountFrequency (%)
Common 10070
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 7740
76.9%
8 1220
 
12.1%
0 953
 
9.5%
9 157
 
1.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 10070
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 7740
76.9%
8 1220
 
12.1%
0 953
 
9.5%
9 157
 
1.6%

q11
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size77.9 KiB
0
7377 
8
1220 
2
1093 
1
 
259

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters9949
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row0
5th row2

Common Values

ValueCountFrequency (%)
0 7377
74.1%
8 1220
 
12.3%
2 1093
 
11.0%
1 259
 
2.6%

Length

2023-03-23T10:34:23.943979image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-23T10:34:24.323992image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 7377
74.1%
8 1220
 
12.3%
2 1093
 
11.0%
1 259
 
2.6%

Most occurring characters

ValueCountFrequency (%)
0 7377
74.1%
8 1220
 
12.3%
2 1093
 
11.0%
1 259
 
2.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 9949
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 7377
74.1%
8 1220
 
12.3%
2 1093
 
11.0%
1 259
 
2.6%

Most occurring scripts

ValueCountFrequency (%)
Common 9949
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 7377
74.1%
8 1220
 
12.3%
2 1093
 
11.0%
1 259
 
2.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9949
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 7377
74.1%
8 1220
 
12.3%
2 1093
 
11.0%
1 259
 
2.6%

q12
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size77.9 KiB
0
8586 
8
1220 
2
 
143

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters9949
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row2

Common Values

ValueCountFrequency (%)
0 8586
86.3%
8 1220
 
12.3%
2 143
 
1.4%

Length

2023-03-23T10:34:24.600228image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-23T10:34:24.947310image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 8586
86.3%
8 1220
 
12.3%
2 143
 
1.4%

Most occurring characters

ValueCountFrequency (%)
0 8586
86.3%
8 1220
 
12.3%
2 143
 
1.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 9949
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 8586
86.3%
8 1220
 
12.3%
2 143
 
1.4%

Most occurring scripts

ValueCountFrequency (%)
Common 9949
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 8586
86.3%
8 1220
 
12.3%
2 143
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9949
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 8586
86.3%
8 1220
 
12.3%
2 143
 
1.4%

q13
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct12
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.427179
Minimum0
Maximum88
Zeros401
Zeros (%)4.0%
Negative0
Negative (%)0.0%
Memory size77.9 KiB
2023-03-23T10:34:25.235529image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q13
median4
Q36
95-th percentile88
Maximum88
Range88
Interquartile range (IQR)3

Descriptive statistics

Standard deviation28.963634
Coefficient of variation (CV)1.8774421
Kurtosis2.4277343
Mean15.427179
Median Absolute Deviation (MAD)1
Skewness2.0975988
Sum153485
Variance838.89209
MonotonicityNot monotonic
2023-03-23T10:34:25.529745image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
4 1997
20.1%
3 1793
18.0%
5 1667
16.8%
88 1363
13.7%
6 947
9.5%
2 831
8.4%
0 401
 
4.0%
1 394
 
4.0%
7 391
 
3.9%
8 134
 
1.3%
Other values (2) 31
 
0.3%
ValueCountFrequency (%)
0 401
 
4.0%
1 394
 
4.0%
2 831
8.4%
3 1793
18.0%
4 1997
20.1%
5 1667
16.8%
6 947
9.5%
7 391
 
3.9%
8 134
 
1.3%
9 18
 
0.2%
ValueCountFrequency (%)
88 1363
13.7%
10 13
 
0.1%
9 18
 
0.2%
8 134
 
1.3%
7 391
 
3.9%
6 947
9.5%
5 1667
16.8%
4 1997
20.1%
3 1793
18.0%
2 831
8.4%

q14
Real number (ℝ)

Distinct23
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.845713
Minimum0
Maximum99
Zeros32
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size77.9 KiB
2023-03-23T10:34:25.691308image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q18
median12
Q317
95-th percentile88
Maximum99
Range99
Interquartile range (IQR)9

Descriptive statistics

Standard deviation30.262928
Coefficient of variation (CV)1.269114
Kurtosis1.1751
Mean23.845713
Median Absolute Deviation (MAD)5
Skewness1.7303085
Sum237241
Variance915.84481
MonotonicityNot monotonic
2023-03-23T10:34:25.831929image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
88 1220
 
12.3%
12 667
 
6.7%
10 660
 
6.6%
8 633
 
6.4%
9 626
 
6.3%
5 535
 
5.4%
11 534
 
5.4%
6 532
 
5.3%
15 506
 
5.1%
13 504
 
5.1%
Other values (13) 3532
35.5%
ValueCountFrequency (%)
0 32
 
0.3%
1 99
 
1.0%
2 204
 
2.1%
3 270
2.7%
4 287
2.9%
5 535
5.4%
6 532
5.3%
7 403
4.1%
8 633
6.4%
9 626
6.3%
ValueCountFrequency (%)
99 429
 
4.3%
88 1220
12.3%
20 426
 
4.3%
19 176
 
1.8%
18 226
 
2.3%
17 243
 
2.4%
16 279
 
2.8%
15 506
5.1%
14 458
 
4.6%
13 504
5.1%

q15
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size77.9 KiB
0
4920 
8
2572 
1
2457 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters9949
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row8
4th row1
5th row8

Common Values

ValueCountFrequency (%)
0 4920
49.5%
8 2572
25.9%
1 2457
24.7%

Length

2023-03-23T10:34:25.970560image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-23T10:34:26.096222image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 4920
49.5%
8 2572
25.9%
1 2457
24.7%

Most occurring characters

ValueCountFrequency (%)
0 4920
49.5%
8 2572
25.9%
1 2457
24.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 9949
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 4920
49.5%
8 2572
25.9%
1 2457
24.7%

Most occurring scripts

ValueCountFrequency (%)
Common 9949
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 4920
49.5%
8 2572
25.9%
1 2457
24.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9949
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 4920
49.5%
8 2572
25.9%
1 2457
24.7%

q16
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.9748718
Minimum0
Maximum8
Zeros4957
Zeros (%)49.8%
Negative0
Negative (%)0.0%
Memory size77.9 KiB
2023-03-23T10:34:26.200943image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q34
95-th percentile8
Maximum8
Range8
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.6847444
Coefficient of variation (CV)1.3594525
Kurtosis0.31126982
Mean1.9748718
Median Absolute Deviation (MAD)1
Skewness1.2513674
Sum19648
Variance7.2078526
MonotonicityNot monotonic
2023-03-23T10:34:26.322617image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 4957
49.8%
4 1539
 
15.5%
1 1270
 
12.8%
8 1220
 
12.3%
3 536
 
5.4%
2 427
 
4.3%
ValueCountFrequency (%)
0 4957
49.8%
1 1270
 
12.8%
2 427
 
4.3%
3 536
 
5.4%
4 1539
 
15.5%
8 1220
 
12.3%
ValueCountFrequency (%)
8 1220
 
12.3%
4 1539
 
15.5%
3 536
 
5.4%
2 427
 
4.3%
1 1270
 
12.8%
0 4957
49.8%

q17
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct12
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.498643
Minimum0
Maximum88
Zeros1017
Zeros (%)10.2%
Negative0
Negative (%)0.0%
Memory size77.9 KiB
2023-03-23T10:34:26.742497image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median3
Q35
95-th percentile88
Maximum88
Range88
Interquartile range (IQR)3

Descriptive statistics

Standard deviation29.337726
Coefficient of variation (CV)2.0234808
Kurtosis2.4251683
Mean14.498643
Median Absolute Deviation (MAD)2
Skewness2.0966835
Sum144247
Variance860.70217
MonotonicityNot monotonic
2023-03-23T10:34:26.864172image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
3 1884
18.9%
2 1711
17.2%
88 1363
13.7%
4 1351
13.6%
1 1118
11.2%
0 1017
10.2%
5 806
8.1%
6 378
 
3.8%
7 211
 
2.1%
8 70
 
0.7%
Other values (2) 40
 
0.4%
ValueCountFrequency (%)
0 1017
10.2%
1 1118
11.2%
2 1711
17.2%
3 1884
18.9%
4 1351
13.6%
5 806
8.1%
6 378
 
3.8%
7 211
 
2.1%
8 70
 
0.7%
9 28
 
0.3%
ValueCountFrequency (%)
88 1363
13.7%
10 12
 
0.1%
9 28
 
0.3%
8 70
 
0.7%
7 211
 
2.1%
6 378
 
3.8%
5 806
8.1%
4 1351
13.6%
3 1884
18.9%
2 1711
17.2%

q18
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size77.9 KiB
1
8373 
8
1220 
2
 
161
10
 
135
9
 
60

Length

Max length2
Median length1
Mean length1.0135692
Min length1

Characters and Unicode

Total characters10084
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row2
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 8373
84.2%
8 1220
 
12.3%
2 161
 
1.6%
10 135
 
1.4%
9 60
 
0.6%

Length

2023-03-23T10:34:26.990831image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-23T10:34:27.134449image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
1 8373
84.2%
8 1220
 
12.3%
2 161
 
1.6%
10 135
 
1.4%
9 60
 
0.6%

Most occurring characters

ValueCountFrequency (%)
1 8508
84.4%
8 1220
 
12.1%
2 161
 
1.6%
0 135
 
1.3%
9 60
 
0.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 10084
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 8508
84.4%
8 1220
 
12.1%
2 161
 
1.6%
0 135
 
1.3%
9 60
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
Common 10084
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 8508
84.4%
8 1220
 
12.1%
2 161
 
1.6%
0 135
 
1.3%
9 60
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 10084
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 8508
84.4%
8 1220
 
12.1%
2 161
 
1.6%
0 135
 
1.3%
9 60
 
0.6%

q19
Categorical

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size77.9 KiB
1
4428 
2
3990 
8
1220 
9
 
184
10
 
127

Length

Max length2
Median length1
Mean length1.0127651
Min length1

Characters and Unicode

Total characters10076
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row2
4th row2
5th row2

Common Values

ValueCountFrequency (%)
1 4428
44.5%
2 3990
40.1%
8 1220
 
12.3%
9 184
 
1.8%
10 127
 
1.3%

Length

2023-03-23T10:34:27.259113image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-23T10:34:27.421680image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
1 4428
44.5%
2 3990
40.1%
8 1220
 
12.3%
9 184
 
1.8%
10 127
 
1.3%

Most occurring characters

ValueCountFrequency (%)
1 4555
45.2%
2 3990
39.6%
8 1220
 
12.1%
9 184
 
1.8%
0 127
 
1.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 10076
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 4555
45.2%
2 3990
39.6%
8 1220
 
12.1%
9 184
 
1.8%
0 127
 
1.3%

Most occurring scripts

ValueCountFrequency (%)
Common 10076
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 4555
45.2%
2 3990
39.6%
8 1220
 
12.1%
9 184
 
1.8%
0 127
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 10076
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 4555
45.2%
2 3990
39.6%
8 1220
 
12.1%
9 184
 
1.8%
0 127
 
1.3%

q20
Categorical

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size77.9 KiB
1
7241 
8
1220 
9
973 
2
 
323
10
 
192

Length

Max length2
Median length1
Mean length1.0192984
Min length1

Characters and Unicode

Total characters10141
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row2
4th row1
5th row9

Common Values

ValueCountFrequency (%)
1 7241
72.8%
8 1220
 
12.3%
9 973
 
9.8%
2 323
 
3.2%
10 192
 
1.9%

Length

2023-03-23T10:34:27.579259image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-23T10:34:27.991159image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
1 7241
72.8%
8 1220
 
12.3%
9 973
 
9.8%
2 323
 
3.2%
10 192
 
1.9%

Most occurring characters

ValueCountFrequency (%)
1 7433
73.3%
8 1220
 
12.0%
9 973
 
9.6%
2 323
 
3.2%
0 192
 
1.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 10141
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 7433
73.3%
8 1220
 
12.0%
9 973
 
9.6%
2 323
 
3.2%
0 192
 
1.9%

Most occurring scripts

ValueCountFrequency (%)
Common 10141
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 7433
73.3%
8 1220
 
12.0%
9 973
 
9.6%
2 323
 
3.2%
0 192
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 10141
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 7433
73.3%
8 1220
 
12.0%
9 973
 
9.6%
2 323
 
3.2%
0 192
 
1.9%

q21
Categorical

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size77.9 KiB
9
5234 
1
2445 
8
1220 
2
746 
10
 
304

Length

Max length2
Median length1
Mean length1.0305558
Min length1

Characters and Unicode

Total characters10253
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row9
3rd row2
4th row1
5th row9

Common Values

ValueCountFrequency (%)
9 5234
52.6%
1 2445
24.6%
8 1220
 
12.3%
2 746
 
7.5%
10 304
 
3.1%

Length

2023-03-23T10:34:28.293350image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-23T10:34:28.622476image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
9 5234
52.6%
1 2445
24.6%
8 1220
 
12.3%
2 746
 
7.5%
10 304
 
3.1%

Most occurring characters

ValueCountFrequency (%)
9 5234
51.0%
1 2749
26.8%
8 1220
 
11.9%
2 746
 
7.3%
0 304
 
3.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 10253
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
9 5234
51.0%
1 2749
26.8%
8 1220
 
11.9%
2 746
 
7.3%
0 304
 
3.0%

Most occurring scripts

ValueCountFrequency (%)
Common 10253
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
9 5234
51.0%
1 2749
26.8%
8 1220
 
11.9%
2 746
 
7.3%
0 304
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 10253
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
9 5234
51.0%
1 2749
26.8%
8 1220
 
11.9%
2 746
 
7.3%
0 304
 
3.0%

qp1
Real number (ℝ)

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.4686903
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size77.9 KiB
2023-03-23T10:34:28.909703image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q18
median8
Q38
95-th percentile8
Maximum9
Range8
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.5003517
Coefficient of variation (CV)0.20088551
Kurtosis5.3671841
Mean7.4686903
Median Absolute Deviation (MAD)0
Skewness-2.6098423
Sum74306
Variance2.2510552
MonotonicityNot monotonic
2023-03-23T10:34:29.185965image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
8 8729
87.7%
3 480
 
4.8%
4 294
 
3.0%
5 258
 
2.6%
2 108
 
1.1%
1 46
 
0.5%
9 34
 
0.3%
ValueCountFrequency (%)
1 46
 
0.5%
2 108
 
1.1%
3 480
 
4.8%
4 294
 
3.0%
5 258
 
2.6%
8 8729
87.7%
9 34
 
0.3%
ValueCountFrequency (%)
9 34
 
0.3%
8 8729
87.7%
5 258
 
2.6%
4 294
 
3.0%
3 480
 
4.8%
2 108
 
1.1%
1 46
 
0.5%

qp2
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size77.9 KiB
8
8729 
2
 
764
3
 
367
1
 
54
9
 
35

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters9949
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row8
2nd row8
3rd row8
4th row8
5th row8

Common Values

ValueCountFrequency (%)
8 8729
87.7%
2 764
 
7.7%
3 367
 
3.7%
1 54
 
0.5%
9 35
 
0.4%

Length

2023-03-23T10:34:29.511095image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-23T10:34:29.740482image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
8 8729
87.7%
2 764
 
7.7%
3 367
 
3.7%
1 54
 
0.5%
9 35
 
0.4%

Most occurring characters

ValueCountFrequency (%)
8 8729
87.7%
2 764
 
7.7%
3 367
 
3.7%
1 54
 
0.5%
9 35
 
0.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 9949
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
8 8729
87.7%
2 764
 
7.7%
3 367
 
3.7%
1 54
 
0.5%
9 35
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
Common 9949
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
8 8729
87.7%
2 764
 
7.7%
3 367
 
3.7%
1 54
 
0.5%
9 35
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9949
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
8 8729
87.7%
2 764
 
7.7%
3 367
 
3.7%
1 54
 
0.5%
9 35
 
0.4%

qp3
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size77.9 KiB
8
8729 
1
 
744
0
 
476

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters9949
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row8
2nd row8
3rd row8
4th row8
5th row8

Common Values

ValueCountFrequency (%)
8 8729
87.7%
1 744
 
7.5%
0 476
 
4.8%

Length

2023-03-23T10:34:29.879110image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-23T10:34:30.023723image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
8 8729
87.7%
1 744
 
7.5%
0 476
 
4.8%

Most occurring characters

ValueCountFrequency (%)
8 8729
87.7%
1 744
 
7.5%
0 476
 
4.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 9949
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
8 8729
87.7%
1 744
 
7.5%
0 476
 
4.8%

Most occurring scripts

ValueCountFrequency (%)
Common 9949
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
8 8729
87.7%
1 744
 
7.5%
0 476
 
4.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9949
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
8 8729
87.7%
1 744
 
7.5%
0 476
 
4.8%

qp4
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size77.9 KiB
8
9205 
2
 
468
3
 
231
1
 
39
9
 
6

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters9949
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row8
2nd row8
3rd row8
4th row8
5th row8

Common Values

ValueCountFrequency (%)
8 9205
92.5%
2 468
 
4.7%
3 231
 
2.3%
1 39
 
0.4%
9 6
 
0.1%

Length

2023-03-23T10:34:30.132433image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-23T10:34:30.277046image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
8 9205
92.5%
2 468
 
4.7%
3 231
 
2.3%
1 39
 
0.4%
9 6
 
0.1%

Most occurring characters

ValueCountFrequency (%)
8 9205
92.5%
2 468
 
4.7%
3 231
 
2.3%
1 39
 
0.4%
9 6
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 9949
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
8 9205
92.5%
2 468
 
4.7%
3 231
 
2.3%
1 39
 
0.4%
9 6
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 9949
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
8 9205
92.5%
2 468
 
4.7%
3 231
 
2.3%
1 39
 
0.4%
9 6
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9949
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
8 9205
92.5%
2 468
 
4.7%
3 231
 
2.3%
1 39
 
0.4%
9 6
 
0.1%

qp5
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size77.9 KiB
8
9910 
1
 
20
2
 
19

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters9949
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row8
2nd row8
3rd row8
4th row8
5th row8

Common Values

ValueCountFrequency (%)
8 9910
99.6%
1 20
 
0.2%
2 19
 
0.2%

Length

2023-03-23T10:34:30.400716image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-23T10:34:30.537350image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
8 9910
99.6%
1 20
 
0.2%
2 19
 
0.2%

Most occurring characters

ValueCountFrequency (%)
8 9910
99.6%
1 20
 
0.2%
2 19
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 9949
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
8 9910
99.6%
1 20
 
0.2%
2 19
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
Common 9949
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
8 9910
99.6%
1 20
 
0.2%
2 19
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9949
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
8 9910
99.6%
1 20
 
0.2%
2 19
 
0.2%

qp6
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size77.9 KiB
8
9718 
2
 
132
1
 
98
9
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters9949
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row8
2nd row8
3rd row8
4th row8
5th row8

Common Values

ValueCountFrequency (%)
8 9718
97.7%
2 132
 
1.3%
1 98
 
1.0%
9 1
 
< 0.1%

Length

2023-03-23T10:34:30.648054image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-23T10:34:30.786683image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
8 9718
97.7%
2 132
 
1.3%
1 98
 
1.0%
9 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
8 9718
97.7%
2 132
 
1.3%
1 98
 
1.0%
9 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 9949
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
8 9718
97.7%
2 132
 
1.3%
1 98
 
1.0%
9 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 9949
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
8 9718
97.7%
2 132
 
1.3%
1 98
 
1.0%
9 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9949
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
8 9718
97.7%
2 132
 
1.3%
1 98
 
1.0%
9 1
 
< 0.1%

qp7
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size77.9 KiB
8
9205 
2
 
483
3
 
214
1
 
39
9
 
8

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters9949
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row8
2nd row8
3rd row8
4th row8
5th row8

Common Values

ValueCountFrequency (%)
8 9205
92.5%
2 483
 
4.9%
3 214
 
2.2%
1 39
 
0.4%
9 8
 
0.1%

Length

2023-03-23T10:34:30.901378image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-23T10:34:31.042003image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
8 9205
92.5%
2 483
 
4.9%
3 214
 
2.2%
1 39
 
0.4%
9 8
 
0.1%

Most occurring characters

ValueCountFrequency (%)
8 9205
92.5%
2 483
 
4.9%
3 214
 
2.2%
1 39
 
0.4%
9 8
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 9949
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
8 9205
92.5%
2 483
 
4.9%
3 214
 
2.2%
1 39
 
0.4%
9 8
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 9949
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
8 9205
92.5%
2 483
 
4.9%
3 214
 
2.2%
1 39
 
0.4%
9 8
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9949
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
8 9205
92.5%
2 483
 
4.9%
3 214
 
2.2%
1 39
 
0.4%
9 8
 
0.1%

qp8
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size77.9 KiB
8
9910 
1
 
28
2
 
11

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters9949
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row8
2nd row8
3rd row8
4th row8
5th row8

Common Values

ValueCountFrequency (%)
8 9910
99.6%
1 28
 
0.3%
2 11
 
0.1%

Length

2023-03-23T10:34:31.155726image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-23T10:34:31.279368image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
8 9910
99.6%
1 28
 
0.3%
2 11
 
0.1%

Most occurring characters

ValueCountFrequency (%)
8 9910
99.6%
1 28
 
0.3%
2 11
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 9949
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
8 9910
99.6%
1 28
 
0.3%
2 11
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 9949
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
8 9910
99.6%
1 28
 
0.3%
2 11
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9949
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
8 9910
99.6%
1 28
 
0.3%
2 11
 
0.1%

qp9
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size77.9 KiB
8
9735 
2
 
129
1
 
85

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters9949
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row8
2nd row8
3rd row8
4th row8
5th row8

Common Values

ValueCountFrequency (%)
8 9735
97.8%
2 129
 
1.3%
1 85
 
0.9%

Length

2023-03-23T10:34:31.382092image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-23T10:34:31.507758image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
8 9735
97.8%
2 129
 
1.3%
1 85
 
0.9%

Most occurring characters

ValueCountFrequency (%)
8 9735
97.8%
2 129
 
1.3%
1 85
 
0.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 9949
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
8 9735
97.8%
2 129
 
1.3%
1 85
 
0.9%

Most occurring scripts

ValueCountFrequency (%)
Common 9949
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
8 9735
97.8%
2 129
 
1.3%
1 85
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9949
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
8 9735
97.8%
2 129
 
1.3%
1 85
 
0.9%

qp10
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size77.9 KiB
8
9205 
2
 
491
3
 
205
1
 
38
9
 
10

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters9949
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row8
2nd row8
3rd row8
4th row8
5th row8

Common Values

ValueCountFrequency (%)
8 9205
92.5%
2 491
 
4.9%
3 205
 
2.1%
1 38
 
0.4%
9 10
 
0.1%

Length

2023-03-23T10:34:31.635420image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-23T10:34:31.966535image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
8 9205
92.5%
2 491
 
4.9%
3 205
 
2.1%
1 38
 
0.4%
9 10
 
0.1%

Most occurring characters

ValueCountFrequency (%)
8 9205
92.5%
2 491
 
4.9%
3 205
 
2.1%
1 38
 
0.4%
9 10
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 9949
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
8 9205
92.5%
2 491
 
4.9%
3 205
 
2.1%
1 38
 
0.4%
9 10
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 9949
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
8 9205
92.5%
2 491
 
4.9%
3 205
 
2.1%
1 38
 
0.4%
9 10
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9949
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
8 9205
92.5%
2 491
 
4.9%
3 205
 
2.1%
1 38
 
0.4%
9 10
 
0.1%

qp11
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size77.9 KiB
8
9911 
1
 
27
2
 
11

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters9949
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row8
2nd row8
3rd row8
4th row8
5th row8

Common Values

ValueCountFrequency (%)
8 9911
99.6%
1 27
 
0.3%
2 11
 
0.1%

Length

2023-03-23T10:34:32.262743image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-23T10:34:32.576936image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
8 9911
99.6%
1 27
 
0.3%
2 11
 
0.1%

Most occurring characters

ValueCountFrequency (%)
8 9911
99.6%
1 27
 
0.3%
2 11
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 9949
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
8 9911
99.6%
1 27
 
0.3%
2 11
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 9949
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
8 9911
99.6%
1 27
 
0.3%
2 11
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9949
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
8 9911
99.6%
1 27
 
0.3%
2 11
 
0.1%

qp12
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size77.9 KiB
8
9744 
2
 
135
1
 
70

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters9949
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row8
2nd row8
3rd row8
4th row8
5th row8

Common Values

ValueCountFrequency (%)
8 9744
97.9%
2 135
 
1.4%
1 70
 
0.7%

Length

2023-03-23T10:34:32.830222image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-23T10:34:33.165326image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
8 9744
97.9%
2 135
 
1.4%
1 70
 
0.7%

Most occurring characters

ValueCountFrequency (%)
8 9744
97.9%
2 135
 
1.4%
1 70
 
0.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 9949
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
8 9744
97.9%
2 135
 
1.4%
1 70
 
0.7%

Most occurring scripts

ValueCountFrequency (%)
Common 9949
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
8 9744
97.9%
2 135
 
1.4%
1 70
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9949
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
8 9744
97.9%
2 135
 
1.4%
1 70
 
0.7%

qp13
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size77.9 KiB
8
9205 
2
 
500
3
 
189
1
 
42
9
 
13

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters9949
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row8
2nd row8
3rd row8
4th row8
5th row8

Common Values

ValueCountFrequency (%)
8 9205
92.5%
2 500
 
5.0%
3 189
 
1.9%
1 42
 
0.4%
9 13
 
0.1%

Length

2023-03-23T10:34:33.487466image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-23T10:34:33.826559image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
8 9205
92.5%
2 500
 
5.0%
3 189
 
1.9%
1 42
 
0.4%
9 13
 
0.1%

Most occurring characters

ValueCountFrequency (%)
8 9205
92.5%
2 500
 
5.0%
3 189
 
1.9%
1 42
 
0.4%
9 13
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 9949
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
8 9205
92.5%
2 500
 
5.0%
3 189
 
1.9%
1 42
 
0.4%
9 13
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 9949
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
8 9205
92.5%
2 500
 
5.0%
3 189
 
1.9%
1 42
 
0.4%
9 13
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9949
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
8 9205
92.5%
2 500
 
5.0%
3 189
 
1.9%
1 42
 
0.4%
9 13
 
0.1%

qp14
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size77.9 KiB
8
9907 
1
 
31
2
 
11

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters9949
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row8
2nd row8
3rd row8
4th row8
5th row8

Common Values

ValueCountFrequency (%)
8 9907
99.6%
1 31
 
0.3%
2 11
 
0.1%

Length

2023-03-23T10:34:34.100825image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-23T10:34:34.403019image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
8 9907
99.6%
1 31
 
0.3%
2 11
 
0.1%

Most occurring characters

ValueCountFrequency (%)
8 9907
99.6%
1 31
 
0.3%
2 11
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 9949
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
8 9907
99.6%
1 31
 
0.3%
2 11
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 9949
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
8 9907
99.6%
1 31
 
0.3%
2 11
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9949
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
8 9907
99.6%
1 31
 
0.3%
2 11
 
0.1%

qp15
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size77.9 KiB
8
9760 
2
 
134
1
 
52
9
 
3

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters9949
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row8
2nd row8
3rd row8
4th row8
5th row8

Common Values

ValueCountFrequency (%)
8 9760
98.1%
2 134
 
1.3%
1 52
 
0.5%
9 3
 
< 0.1%

Length

2023-03-23T10:34:34.635396image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-23T10:34:34.767049image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
8 9760
98.1%
2 134
 
1.3%
1 52
 
0.5%
9 3
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
8 9760
98.1%
2 134
 
1.3%
1 52
 
0.5%
9 3
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 9949
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
8 9760
98.1%
2 134
 
1.3%
1 52
 
0.5%
9 3
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 9949
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
8 9760
98.1%
2 134
 
1.3%
1 52
 
0.5%
9 3
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9949
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
8 9760
98.1%
2 134
 
1.3%
1 52
 
0.5%
9 3
 
< 0.1%

qp16
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size77.9 KiB
8
9205 
2
 
478
3
 
198
1
 
48
9
 
20

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters9949
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row8
2nd row8
3rd row8
4th row8
5th row8

Common Values

ValueCountFrequency (%)
8 9205
92.5%
2 478
 
4.8%
3 198
 
2.0%
1 48
 
0.5%
9 20
 
0.2%

Length

2023-03-23T10:34:34.879743image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-23T10:34:35.048290image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
8 9205
92.5%
2 478
 
4.8%
3 198
 
2.0%
1 48
 
0.5%
9 20
 
0.2%

Most occurring characters

ValueCountFrequency (%)
8 9205
92.5%
2 478
 
4.8%
3 198
 
2.0%
1 48
 
0.5%
9 20
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 9949
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
8 9205
92.5%
2 478
 
4.8%
3 198
 
2.0%
1 48
 
0.5%
9 20
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
Common 9949
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
8 9205
92.5%
2 478
 
4.8%
3 198
 
2.0%
1 48
 
0.5%
9 20
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9949
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
8 9205
92.5%
2 478
 
4.8%
3 198
 
2.0%
1 48
 
0.5%
9 20
 
0.2%

qp17
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size77.9 KiB
8
9901 
1
 
35
2
 
13

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters9949
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row8
2nd row8
3rd row8
4th row8
5th row8

Common Values

ValueCountFrequency (%)
8 9901
99.5%
1 35
 
0.4%
2 13
 
0.1%

Length

2023-03-23T10:34:35.162984image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-23T10:34:35.305605image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
8 9901
99.5%
1 35
 
0.4%
2 13
 
0.1%

Most occurring characters

ValueCountFrequency (%)
8 9901
99.5%
1 35
 
0.4%
2 13
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 9949
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
8 9901
99.5%
1 35
 
0.4%
2 13
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 9949
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
8 9901
99.5%
1 35
 
0.4%
2 13
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9949
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
8 9901
99.5%
1 35
 
0.4%
2 13
 
0.1%

qp18
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size77.9 KiB
8
9751 
2
 
144
1
 
54

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters9949
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row8
2nd row8
3rd row8
4th row8
5th row8

Common Values

ValueCountFrequency (%)
8 9751
98.0%
2 144
 
1.4%
1 54
 
0.5%

Length

2023-03-23T10:34:35.408328image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-23T10:34:35.531997image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
8 9751
98.0%
2 144
 
1.4%
1 54
 
0.5%

Most occurring characters

ValueCountFrequency (%)
8 9751
98.0%
2 144
 
1.4%
1 54
 
0.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 9949
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
8 9751
98.0%
2 144
 
1.4%
1 54
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
Common 9949
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
8 9751
98.0%
2 144
 
1.4%
1 54
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9949
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
8 9751
98.0%
2 144
 
1.4%
1 54
 
0.5%

qp19
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size77.9 KiB
8
9205 
2
 
457
3
 
230
1
 
40
9
 
17

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters9949
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row8
2nd row8
3rd row8
4th row8
5th row8

Common Values

ValueCountFrequency (%)
8 9205
92.5%
2 457
 
4.6%
3 230
 
2.3%
1 40
 
0.4%
9 17
 
0.2%

Length

2023-03-23T10:34:35.674618image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-23T10:34:35.808259image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
8 9205
92.5%
2 457
 
4.6%
3 230
 
2.3%
1 40
 
0.4%
9 17
 
0.2%

Most occurring characters

ValueCountFrequency (%)
8 9205
92.5%
2 457
 
4.6%
3 230
 
2.3%
1 40
 
0.4%
9 17
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 9949
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
8 9205
92.5%
2 457
 
4.6%
3 230
 
2.3%
1 40
 
0.4%
9 17
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
Common 9949
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
8 9205
92.5%
2 457
 
4.6%
3 230
 
2.3%
1 40
 
0.4%
9 17
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9949
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
8 9205
92.5%
2 457
 
4.6%
3 230
 
2.3%
1 40
 
0.4%
9 17
 
0.2%

qp20
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size77.9 KiB
8
9909 
1
 
31
2
 
9

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters9949
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row8
2nd row8
3rd row8
4th row8
5th row8

Common Values

ValueCountFrequency (%)
8 9909
99.6%
1 31
 
0.3%
2 9
 
0.1%

Length

2023-03-23T10:34:35.927938image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-23T10:34:36.052607image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
8 9909
99.6%
1 31
 
0.3%
2 9
 
0.1%

Most occurring characters

ValueCountFrequency (%)
8 9909
99.6%
1 31
 
0.3%
2 9
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 9949
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
8 9909
99.6%
1 31
 
0.3%
2 9
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 9949
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
8 9909
99.6%
1 31
 
0.3%
2 9
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9949
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
8 9909
99.6%
1 31
 
0.3%
2 9
 
0.1%

qp21
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size77.9 KiB
8
9719 
2
 
145
1
 
84
9
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters9949
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row8
2nd row8
3rd row8
4th row8
5th row8

Common Values

ValueCountFrequency (%)
8 9719
97.7%
2 145
 
1.5%
1 84
 
0.8%
9 1
 
< 0.1%

Length

2023-03-23T10:34:36.161317image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-23T10:34:36.326872image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
8 9719
97.7%
2 145
 
1.5%
1 84
 
0.8%
9 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
8 9719
97.7%
2 145
 
1.5%
1 84
 
0.8%
9 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 9949
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
8 9719
97.7%
2 145
 
1.5%
1 84
 
0.8%
9 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 9949
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
8 9719
97.7%
2 145
 
1.5%
1 84
 
0.8%
9 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9949
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
8 9719
97.7%
2 145
 
1.5%
1 84
 
0.8%
9 1
 
< 0.1%

qp22
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size77.9 KiB
8
9205 
2
 
462
3
 
231
1
 
36
9
 
15

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters9949
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row8
2nd row8
3rd row8
4th row8
5th row8

Common Values

ValueCountFrequency (%)
8 9205
92.5%
2 462
 
4.6%
3 231
 
2.3%
1 36
 
0.4%
9 15
 
0.2%

Length

2023-03-23T10:34:36.433588image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-23T10:34:36.587176image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
8 9205
92.5%
2 462
 
4.6%
3 231
 
2.3%
1 36
 
0.4%
9 15
 
0.2%

Most occurring characters

ValueCountFrequency (%)
8 9205
92.5%
2 462
 
4.6%
3 231
 
2.3%
1 36
 
0.4%
9 15
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 9949
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
8 9205
92.5%
2 462
 
4.6%
3 231
 
2.3%
1 36
 
0.4%
9 15
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
Common 9949
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
8 9205
92.5%
2 462
 
4.6%
3 231
 
2.3%
1 36
 
0.4%
9 15
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9949
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
8 9205
92.5%
2 462
 
4.6%
3 231
 
2.3%
1 36
 
0.4%
9 15
 
0.2%

qp23
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size77.9 KiB
8
9913 
1
 
28
2
 
8

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters9949
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row8
2nd row8
3rd row8
4th row8
5th row8

Common Values

ValueCountFrequency (%)
8 9913
99.6%
1 28
 
0.3%
2 8
 
0.1%

Length

2023-03-23T10:34:36.830532image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-23T10:34:37.200547image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
8 9913
99.6%
1 28
 
0.3%
2 8
 
0.1%

Most occurring characters

ValueCountFrequency (%)
8 9913
99.6%
1 28
 
0.3%
2 8
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 9949
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
8 9913
99.6%
1 28
 
0.3%
2 8
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 9949
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
8 9913
99.6%
1 28
 
0.3%
2 8
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9949
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
8 9913
99.6%
1 28
 
0.3%
2 8
 
0.1%

qp24
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size77.9 KiB
8
9718 
2
 
142
1
 
87
9
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters9949
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row8
2nd row8
3rd row8
4th row8
5th row8

Common Values

ValueCountFrequency (%)
8 9718
97.7%
2 142
 
1.4%
1 87
 
0.9%
9 2
 
< 0.1%

Length

2023-03-23T10:34:37.523676image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-23T10:34:37.862773image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
8 9718
97.7%
2 142
 
1.4%
1 87
 
0.9%
9 2
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
8 9718
97.7%
2 142
 
1.4%
1 87
 
0.9%
9 2
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 9949
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
8 9718
97.7%
2 142
 
1.4%
1 87
 
0.9%
9 2
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 9949
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
8 9718
97.7%
2 142
 
1.4%
1 87
 
0.9%
9 2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9949
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
8 9718
97.7%
2 142
 
1.4%
1 87
 
0.9%
9 2
 
< 0.1%

qp25
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size77.9 KiB
8
9205 
2
 
525
3
 
164
1
 
37
9
 
18

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters9949
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row8
2nd row8
3rd row8
4th row8
5th row8

Common Values

ValueCountFrequency (%)
8 9205
92.5%
2 525
 
5.3%
3 164
 
1.6%
1 37
 
0.4%
9 18
 
0.2%

Length

2023-03-23T10:34:38.141025image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-23T10:34:38.464160image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
8 9205
92.5%
2 525
 
5.3%
3 164
 
1.6%
1 37
 
0.4%
9 18
 
0.2%

Most occurring characters

ValueCountFrequency (%)
8 9205
92.5%
2 525
 
5.3%
3 164
 
1.6%
1 37
 
0.4%
9 18
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 9949
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
8 9205
92.5%
2 525
 
5.3%
3 164
 
1.6%
1 37
 
0.4%
9 18
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
Common 9949
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
8 9205
92.5%
2 525
 
5.3%
3 164
 
1.6%
1 37
 
0.4%
9 18
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9949
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
8 9205
92.5%
2 525
 
5.3%
3 164
 
1.6%
1 37
 
0.4%
9 18
 
0.2%

qp26
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size77.9 KiB
8
9912 
1
 
26
2
 
11

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters9949
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row8
2nd row8
3rd row8
4th row8
5th row8

Common Values

ValueCountFrequency (%)
8 9912
99.6%
1 26
 
0.3%
2 11
 
0.1%

Length

2023-03-23T10:34:38.742419image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-23T10:34:39.053585image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
8 9912
99.6%
1 26
 
0.3%
2 11
 
0.1%

Most occurring characters

ValueCountFrequency (%)
8 9912
99.6%
1 26
 
0.3%
2 11
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 9949
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
8 9912
99.6%
1 26
 
0.3%
2 11
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 9949
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
8 9912
99.6%
1 26
 
0.3%
2 11
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9949
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
8 9912
99.6%
1 26
 
0.3%
2 11
 
0.1%

qp27
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size77.9 KiB
8
9785 
2
 
127
1
 
35
9
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters9949
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row8
2nd row8
3rd row8
4th row8
5th row8

Common Values

ValueCountFrequency (%)
8 9785
98.4%
2 127
 
1.3%
1 35
 
0.4%
9 2
 
< 0.1%

Length

2023-03-23T10:34:39.318877image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-23T10:34:39.639019image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
8 9785
98.4%
2 127
 
1.3%
1 35
 
0.4%
9 2
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
8 9785
98.4%
2 127
 
1.3%
1 35
 
0.4%
9 2
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 9949
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
8 9785
98.4%
2 127
 
1.3%
1 35
 
0.4%
9 2
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 9949
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
8 9785
98.4%
2 127
 
1.3%
1 35
 
0.4%
9 2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9949
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
8 9785
98.4%
2 127
 
1.3%
1 35
 
0.4%
9 2
 
< 0.1%

qp28
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size77.9 KiB
8
9205 
2
 
495
3
 
188
1
 
35
9
 
26

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters9949
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row8
2nd row8
3rd row8
4th row8
5th row8

Common Values

ValueCountFrequency (%)
8 9205
92.5%
2 495
 
5.0%
3 188
 
1.9%
1 35
 
0.4%
9 26
 
0.3%

Length

2023-03-23T10:34:39.754709image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-23T10:34:39.889351image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
8 9205
92.5%
2 495
 
5.0%
3 188
 
1.9%
1 35
 
0.4%
9 26
 
0.3%

Most occurring characters

ValueCountFrequency (%)
8 9205
92.5%
2 495
 
5.0%
3 188
 
1.9%
1 35
 
0.4%
9 26
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 9949
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
8 9205
92.5%
2 495
 
5.0%
3 188
 
1.9%
1 35
 
0.4%
9 26
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
Common 9949
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
8 9205
92.5%
2 495
 
5.0%
3 188
 
1.9%
1 35
 
0.4%
9 26
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9949
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
8 9205
92.5%
2 495
 
5.0%
3 188
 
1.9%
1 35
 
0.4%
9 26
 
0.3%

qp29
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size77.9 KiB
8
9914 
1
 
29
2
 
6

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters9949
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row8
2nd row8
3rd row8
4th row8
5th row8

Common Values

ValueCountFrequency (%)
8 9914
99.6%
1 29
 
0.3%
2 6
 
0.1%

Length

2023-03-23T10:34:40.006040image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-23T10:34:40.132698image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
8 9914
99.6%
1 29
 
0.3%
2 6
 
0.1%

Most occurring characters

ValueCountFrequency (%)
8 9914
99.6%
1 29
 
0.3%
2 6
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 9949
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
8 9914
99.6%
1 29
 
0.3%
2 6
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 9949
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
8 9914
99.6%
1 29
 
0.3%
2 6
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9949
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
8 9914
99.6%
1 29
 
0.3%
2 6
 
0.1%

qp30
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size77.9 KiB
8
9761 
2
 
148
1
 
36
9
 
4

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters9949
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row8
2nd row8
3rd row8
4th row8
5th row8

Common Values

ValueCountFrequency (%)
8 9761
98.1%
2 148
 
1.5%
1 36
 
0.4%
9 4
 
< 0.1%

Length

2023-03-23T10:34:40.235424image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-23T10:34:40.370064image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
8 9761
98.1%
2 148
 
1.5%
1 36
 
0.4%
9 4
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
8 9761
98.1%
2 148
 
1.5%
1 36
 
0.4%
9 4
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 9949
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
8 9761
98.1%
2 148
 
1.5%
1 36
 
0.4%
9 4
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 9949
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
8 9761
98.1%
2 148
 
1.5%
1 36
 
0.4%
9 4
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9949
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
8 9761
98.1%
2 148
 
1.5%
1 36
 
0.4%
9 4
 
< 0.1%

qp31
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size77.9 KiB
8
9205 
2
 
479
3
 
208
1
 
37
9
 
20

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters9949
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row8
2nd row8
3rd row8
4th row8
5th row8

Common Values

ValueCountFrequency (%)
8 9205
92.5%
2 479
 
4.8%
3 208
 
2.1%
1 37
 
0.4%
9 20
 
0.2%

Length

2023-03-23T10:34:40.485305image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-23T10:34:40.623971image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
8 9205
92.5%
2 479
 
4.8%
3 208
 
2.1%
1 37
 
0.4%
9 20
 
0.2%

Most occurring characters

ValueCountFrequency (%)
8 9205
92.5%
2 479
 
4.8%
3 208
 
2.1%
1 37
 
0.4%
9 20
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 9949
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
8 9205
92.5%
2 479
 
4.8%
3 208
 
2.1%
1 37
 
0.4%
9 20
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
Common 9949
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
8 9205
92.5%
2 479
 
4.8%
3 208
 
2.1%
1 37
 
0.4%
9 20
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9949
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
8 9205
92.5%
2 479
 
4.8%
3 208
 
2.1%
1 37
 
0.4%
9 20
 
0.2%

qp32
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size77.9 KiB
8
9912 
1
 
31
2
 
6

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters9949
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row8
2nd row8
3rd row8
4th row8
5th row8

Common Values

ValueCountFrequency (%)
8 9912
99.6%
1 31
 
0.3%
2 6
 
0.1%

Length

2023-03-23T10:34:40.741621image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-23T10:34:40.870276image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
8 9912
99.6%
1 31
 
0.3%
2 6
 
0.1%

Most occurring characters

ValueCountFrequency (%)
8 9912
99.6%
1 31
 
0.3%
2 6
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 9949
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
8 9912
99.6%
1 31
 
0.3%
2 6
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 9949
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
8 9912
99.6%
1 31
 
0.3%
2 6
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9949
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
8 9912
99.6%
1 31
 
0.3%
2 6
 
0.1%

qp33
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size77.9 KiB
8
9741 
2
 
155
1
 
51
9
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters9949
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row8
2nd row8
3rd row8
4th row8
5th row8

Common Values

ValueCountFrequency (%)
8 9741
97.9%
2 155
 
1.6%
1 51
 
0.5%
9 2
 
< 0.1%

Length

2023-03-23T10:34:40.975993image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-23T10:34:41.109636image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
8 9741
97.9%
2 155
 
1.6%
1 51
 
0.5%
9 2
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
8 9741
97.9%
2 155
 
1.6%
1 51
 
0.5%
9 2
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 9949
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
8 9741
97.9%
2 155
 
1.6%
1 51
 
0.5%
9 2
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 9949
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
8 9741
97.9%
2 155
 
1.6%
1 51
 
0.5%
9 2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9949
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
8 9741
97.9%
2 155
 
1.6%
1 51
 
0.5%
9 2
 
< 0.1%

qp34
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size77.9 KiB
8
9205 
2
 
494
3
 
195
1
 
34
9
 
21

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters9949
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row8
2nd row8
3rd row8
4th row8
5th row8

Common Values

ValueCountFrequency (%)
8 9205
92.5%
2 494
 
5.0%
3 195
 
2.0%
1 34
 
0.3%
9 21
 
0.2%

Length

2023-03-23T10:34:41.222335image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-23T10:34:41.357972image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
8 9205
92.5%
2 494
 
5.0%
3 195
 
2.0%
1 34
 
0.3%
9 21
 
0.2%

Most occurring characters

ValueCountFrequency (%)
8 9205
92.5%
2 494
 
5.0%
3 195
 
2.0%
1 34
 
0.3%
9 21
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 9949
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
8 9205
92.5%
2 494
 
5.0%
3 195
 
2.0%
1 34
 
0.3%
9 21
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
Common 9949
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
8 9205
92.5%
2 494
 
5.0%
3 195
 
2.0%
1 34
 
0.3%
9 21
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9949
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
8 9205
92.5%
2 494
 
5.0%
3 195
 
2.0%
1 34
 
0.3%
9 21
 
0.2%

qp35
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size77.9 KiB
8
9915 
1
 
29
2
 
5

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters9949
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row8
2nd row8
3rd row8
4th row8
5th row8

Common Values

ValueCountFrequency (%)
8 9915
99.7%
1 29
 
0.3%
2 5
 
0.1%

Length

2023-03-23T10:34:41.501588image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-23T10:34:41.652190image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
8 9915
99.7%
1 29
 
0.3%
2 5
 
0.1%

Most occurring characters

ValueCountFrequency (%)
8 9915
99.7%
1 29
 
0.3%
2 5
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 9949
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
8 9915
99.7%
1 29
 
0.3%
2 5
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 9949
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
8 9915
99.7%
1 29
 
0.3%
2 5
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9949
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
8 9915
99.7%
1 29
 
0.3%
2 5
 
0.1%

qp36
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size77.9 KiB
8
9754 
2
 
151
1
 
44

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters9949
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row8
2nd row8
3rd row8
4th row8
5th row8

Common Values

ValueCountFrequency (%)
8 9754
98.0%
2 151
 
1.5%
1 44
 
0.4%

Length

2023-03-23T10:34:42.215679image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-23T10:34:42.518876image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
8 9754
98.0%
2 151
 
1.5%
1 44
 
0.4%

Most occurring characters

ValueCountFrequency (%)
8 9754
98.0%
2 151
 
1.5%
1 44
 
0.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 9949
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
8 9754
98.0%
2 151
 
1.5%
1 44
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
Common 9949
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
8 9754
98.0%
2 151
 
1.5%
1 44
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9949
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
8 9754
98.0%
2 151
 
1.5%
1 44
 
0.4%

qp37
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size77.9 KiB
8
9205 
2
 
501
3
 
180
1
 
44
9
 
19

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters9949
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row8
2nd row8
3rd row8
4th row8
5th row8

Common Values

ValueCountFrequency (%)
8 9205
92.5%
2 501
 
5.0%
3 180
 
1.8%
1 44
 
0.4%
9 19
 
0.2%

Length

2023-03-23T10:34:42.767226image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-23T10:34:43.138214image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
8 9205
92.5%
2 501
 
5.0%
3 180
 
1.8%
1 44
 
0.4%
9 19
 
0.2%

Most occurring characters

ValueCountFrequency (%)
8 9205
92.5%
2 501
 
5.0%
3 180
 
1.8%
1 44
 
0.4%
9 19
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 9949
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
8 9205
92.5%
2 501
 
5.0%
3 180
 
1.8%
1 44
 
0.4%
9 19
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
Common 9949
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
8 9205
92.5%
2 501
 
5.0%
3 180
 
1.8%
1 44
 
0.4%
9 19
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9949
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
8 9205
92.5%
2 501
 
5.0%
3 180
 
1.8%
1 44
 
0.4%
9 19
 
0.2%

qp38
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size77.9 KiB
8
9905 
1
 
34
2
 
9
9
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters9949
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row8
2nd row8
3rd row8
4th row8
5th row8

Common Values

ValueCountFrequency (%)
8 9905
99.6%
1 34
 
0.3%
2 9
 
0.1%
9 1
 
< 0.1%

Length

2023-03-23T10:34:43.456369image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-23T10:34:43.812413image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
8 9905
99.6%
1 34
 
0.3%
2 9
 
0.1%
9 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
8 9905
99.6%
1 34
 
0.3%
2 9
 
0.1%
9 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 9949
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
8 9905
99.6%
1 34
 
0.3%
2 9
 
0.1%
9 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 9949
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
8 9905
99.6%
1 34
 
0.3%
2 9
 
0.1%
9 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9949
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
8 9905
99.6%
1 34
 
0.3%
2 9
 
0.1%
9 1
 
< 0.1%

qp39
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size77.9 KiB
8
9769 
2
 
134
1
 
44
9
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters9949
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row8
2nd row8
3rd row8
4th row8
5th row8

Common Values

ValueCountFrequency (%)
8 9769
98.2%
2 134
 
1.3%
1 44
 
0.4%
9 2
 
< 0.1%

Length

2023-03-23T10:34:44.078704image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-23T10:34:44.711009image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
8 9769
98.2%
2 134
 
1.3%
1 44
 
0.4%
9 2
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
8 9769
98.2%
2 134
 
1.3%
1 44
 
0.4%
9 2
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 9949
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
8 9769
98.2%
2 134
 
1.3%
1 44
 
0.4%
9 2
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 9949
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
8 9769
98.2%
2 134
 
1.3%
1 44
 
0.4%
9 2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9949
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
8 9769
98.2%
2 134
 
1.3%
1 44
 
0.4%
9 2
 
< 0.1%

qp40
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size77.9 KiB
8
9205 
2
 
497
3
 
179
1
 
47
9
 
21

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters9949
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row8
2nd row8
3rd row8
4th row8
5th row8

Common Values

ValueCountFrequency (%)
8 9205
92.5%
2 497
 
5.0%
3 179
 
1.8%
1 47
 
0.5%
9 21
 
0.2%

Length

2023-03-23T10:34:44.817724image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-23T10:34:44.976306image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
8 9205
92.5%
2 497
 
5.0%
3 179
 
1.8%
1 47
 
0.5%
9 21
 
0.2%

Most occurring characters

ValueCountFrequency (%)
8 9205
92.5%
2 497
 
5.0%
3 179
 
1.8%
1 47
 
0.5%
9 21
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 9949
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
8 9205
92.5%
2 497
 
5.0%
3 179
 
1.8%
1 47
 
0.5%
9 21
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
Common 9949
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
8 9205
92.5%
2 497
 
5.0%
3 179
 
1.8%
1 47
 
0.5%
9 21
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9949
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
8 9205
92.5%
2 497
 
5.0%
3 179
 
1.8%
1 47
 
0.5%
9 21
 
0.2%

qp41
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size77.9 KiB
8
9902 
1
 
36
2
 
11

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters9949
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row8
2nd row8
3rd row8
4th row8
5th row8

Common Values

ValueCountFrequency (%)
8 9902
99.5%
1 36
 
0.4%
2 11
 
0.1%

Length

2023-03-23T10:34:45.111938image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-23T10:34:45.254556image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
8 9902
99.5%
1 36
 
0.4%
2 11
 
0.1%

Most occurring characters

ValueCountFrequency (%)
8 9902
99.5%
1 36
 
0.4%
2 11
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 9949
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
8 9902
99.5%
1 36
 
0.4%
2 11
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 9949
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
8 9902
99.5%
1 36
 
0.4%
2 11
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9949
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
8 9902
99.5%
1 36
 
0.4%
2 11
 
0.1%

qp42
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size77.9 KiB
8
9770 
2
 
139
1
 
39
9
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters9949
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row8
2nd row8
3rd row8
4th row8
5th row8

Common Values

ValueCountFrequency (%)
8 9770
98.2%
2 139
 
1.4%
1 39
 
0.4%
9 1
 
< 0.1%

Length

2023-03-23T10:34:45.367254image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-23T10:34:45.505883image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
8 9770
98.2%
2 139
 
1.4%
1 39
 
0.4%
9 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
8 9770
98.2%
2 139
 
1.4%
1 39
 
0.4%
9 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 9949
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
8 9770
98.2%
2 139
 
1.4%
1 39
 
0.4%
9 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 9949
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
8 9770
98.2%
2 139
 
1.4%
1 39
 
0.4%
9 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9949
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
8 9770
98.2%
2 139
 
1.4%
1 39
 
0.4%
9 1
 
< 0.1%

qp43
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size77.9 KiB
8
9205 
2
 
495
3
 
182
1
 
46
9
 
21

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters9949
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row8
2nd row8
3rd row8
4th row8
5th row8

Common Values

ValueCountFrequency (%)
8 9205
92.5%
2 495
 
5.0%
3 182
 
1.8%
1 46
 
0.5%
9 21
 
0.2%

Length

2023-03-23T10:34:45.622578image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-23T10:34:45.770178image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
8 9205
92.5%
2 495
 
5.0%
3 182
 
1.8%
1 46
 
0.5%
9 21
 
0.2%

Most occurring characters

ValueCountFrequency (%)
8 9205
92.5%
2 495
 
5.0%
3 182
 
1.8%
1 46
 
0.5%
9 21
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 9949
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
8 9205
92.5%
2 495
 
5.0%
3 182
 
1.8%
1 46
 
0.5%
9 21
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
Common 9949
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
8 9205
92.5%
2 495
 
5.0%
3 182
 
1.8%
1 46
 
0.5%
9 21
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9949
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
8 9205
92.5%
2 495
 
5.0%
3 182
 
1.8%
1 46
 
0.5%
9 21
 
0.2%

qp44
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size77.9 KiB
8
9903 
1
 
31
2
 
14
9
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters9949
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row8
2nd row8
3rd row8
4th row8
5th row8

Common Values

ValueCountFrequency (%)
8 9903
99.5%
1 31
 
0.3%
2 14
 
0.1%
9 1
 
< 0.1%

Length

2023-03-23T10:34:45.895842image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-23T10:34:46.047457image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
8 9903
99.5%
1 31
 
0.3%
2 14
 
0.1%
9 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
8 9903
99.5%
1 31
 
0.3%
2 14
 
0.1%
9 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 9949
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
8 9903
99.5%
1 31
 
0.3%
2 14
 
0.1%
9 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 9949
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
8 9903
99.5%
1 31
 
0.3%
2 14
 
0.1%
9 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9949
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
8 9903
99.5%
1 31
 
0.3%
2 14
 
0.1%
9 1
 
< 0.1%

qp45
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size77.9 KiB
8
9767 
2
 
142
1
 
37
9
 
3

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters9949
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row8
2nd row8
3rd row8
4th row8
5th row8

Common Values

ValueCountFrequency (%)
8 9767
98.2%
2 142
 
1.4%
1 37
 
0.4%
9 3
 
< 0.1%

Length

2023-03-23T10:34:46.161133image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-23T10:34:46.298764image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
8 9767
98.2%
2 142
 
1.4%
1 37
 
0.4%
9 3
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
8 9767
98.2%
2 142
 
1.4%
1 37
 
0.4%
9 3
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 9949
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
8 9767
98.2%
2 142
 
1.4%
1 37
 
0.4%
9 3
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 9949
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
8 9767
98.2%
2 142
 
1.4%
1 37
 
0.4%
9 3
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9949
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
8 9767
98.2%
2 142
 
1.4%
1 37
 
0.4%
9 3
 
< 0.1%

qp46
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size77.9 KiB
8
9205 
2
 
490
3
 
198
1
 
32
9
 
24

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters9949
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row8
2nd row8
3rd row8
4th row8
5th row8

Common Values

ValueCountFrequency (%)
8 9205
92.5%
2 490
 
4.9%
3 198
 
2.0%
1 32
 
0.3%
9 24
 
0.2%

Length

2023-03-23T10:34:46.415452image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-23T10:34:46.561094image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
8 9205
92.5%
2 490
 
4.9%
3 198
 
2.0%
1 32
 
0.3%
9 24
 
0.2%

Most occurring characters

ValueCountFrequency (%)
8 9205
92.5%
2 490
 
4.9%
3 198
 
2.0%
1 32
 
0.3%
9 24
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 9949
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
8 9205
92.5%
2 490
 
4.9%
3 198
 
2.0%
1 32
 
0.3%
9 24
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
Common 9949
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
8 9205
92.5%
2 490
 
4.9%
3 198
 
2.0%
1 32
 
0.3%
9 24
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9949
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
8 9205
92.5%
2 490
 
4.9%
3 198
 
2.0%
1 32
 
0.3%
9 24
 
0.2%

qp47
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size77.9 KiB
8
9917 
1
 
28
2
 
4

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters9949
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row8
2nd row8
3rd row8
4th row8
5th row8

Common Values

ValueCountFrequency (%)
8 9917
99.7%
1 28
 
0.3%
2 4
 
< 0.1%

Length

2023-03-23T10:34:46.692749image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-23T10:34:46.991911image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
8 9917
99.7%
1 28
 
0.3%
2 4
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
8 9917
99.7%
1 28
 
0.3%
2 4
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 9949
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
8 9917
99.7%
1 28
 
0.3%
2 4
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 9949
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
8 9917
99.7%
1 28
 
0.3%
2 4
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9949
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
8 9917
99.7%
1 28
 
0.3%
2 4
 
< 0.1%

qp48
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size77.9 KiB
8
9751 
2
 
158
1
 
38
9
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters9949
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row8
2nd row8
3rd row8
4th row8
5th row8

Common Values

ValueCountFrequency (%)
8 9751
98.0%
2 158
 
1.6%
1 38
 
0.4%
9 2
 
< 0.1%

Length

2023-03-23T10:34:47.240249image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-23T10:34:47.569369image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
8 9751
98.0%
2 158
 
1.6%
1 38
 
0.4%
9 2
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
8 9751
98.0%
2 158
 
1.6%
1 38
 
0.4%
9 2
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 9949
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
8 9751
98.0%
2 158
 
1.6%
1 38
 
0.4%
9 2
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 9949
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
8 9751
98.0%
2 158
 
1.6%
1 38
 
0.4%
9 2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9949
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
8 9751
98.0%
2 158
 
1.6%
1 38
 
0.4%
9 2
 
< 0.1%

qp49
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size77.9 KiB
8
9205 
2
 
528
3
 
157
1
 
38
9
 
21

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters9949
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row8
2nd row8
3rd row8
4th row8
5th row8

Common Values

ValueCountFrequency (%)
8 9205
92.5%
2 528
 
5.3%
3 157
 
1.6%
1 38
 
0.4%
9 21
 
0.2%

Length

2023-03-23T10:34:47.840649image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-23T10:34:48.166771image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
8 9205
92.5%
2 528
 
5.3%
3 157
 
1.6%
1 38
 
0.4%
9 21
 
0.2%

Most occurring characters

ValueCountFrequency (%)
8 9205
92.5%
2 528
 
5.3%
3 157
 
1.6%
1 38
 
0.4%
9 21
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 9949
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
8 9205
92.5%
2 528
 
5.3%
3 157
 
1.6%
1 38
 
0.4%
9 21
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
Common 9949
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
8 9205
92.5%
2 528
 
5.3%
3 157
 
1.6%
1 38
 
0.4%
9 21
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9949
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
8 9205
92.5%
2 528
 
5.3%
3 157
 
1.6%
1 38
 
0.4%
9 21
 
0.2%

qp50
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size77.9 KiB
8
9911 
1
 
28
2
 
10

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters9949
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row8
2nd row8
3rd row8
4th row8
5th row8

Common Values

ValueCountFrequency (%)
8 9911
99.6%
1 28
 
0.3%
2 10
 
0.1%

Length

2023-03-23T10:34:48.436089image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-23T10:34:48.709329image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
8 9911
99.6%
1 28
 
0.3%
2 10
 
0.1%

Most occurring characters

ValueCountFrequency (%)
8 9911
99.6%
1 28
 
0.3%
2 10
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 9949
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
8 9911
99.6%
1 28
 
0.3%
2 10
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 9949
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
8 9911
99.6%
1 28
 
0.3%
2 10
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9949
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
8 9911
99.6%
1 28
 
0.3%
2 10
 
0.1%

qp51
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size77.9 KiB
8
9792 
2
 
125
1
 
31
9
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters9949
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row8
2nd row8
3rd row8
4th row8
5th row8

Common Values

ValueCountFrequency (%)
8 9792
98.4%
2 125
 
1.3%
1 31
 
0.3%
9 1
 
< 0.1%

Length

2023-03-23T10:34:48.820027image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-23T10:34:48.961645image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
8 9792
98.4%
2 125
 
1.3%
1 31
 
0.3%
9 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
8 9792
98.4%
2 125
 
1.3%
1 31
 
0.3%
9 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 9949
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
8 9792
98.4%
2 125
 
1.3%
1 31
 
0.3%
9 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 9949
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
8 9792
98.4%
2 125
 
1.3%
1 31
 
0.3%
9 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9949
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
8 9792
98.4%
2 125
 
1.3%
1 31
 
0.3%
9 1
 
< 0.1%

qp52
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size77.9 KiB
8
9205 
0
 
629
1
 
98
9
 
17

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters9949
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row8
2nd row8
3rd row8
4th row8
5th row8

Common Values

ValueCountFrequency (%)
8 9205
92.5%
0 629
 
6.3%
1 98
 
1.0%
9 17
 
0.2%

Length

2023-03-23T10:34:49.076349image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-23T10:34:49.206005image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
8 9205
92.5%
0 629
 
6.3%
1 98
 
1.0%
9 17
 
0.2%

Most occurring characters

ValueCountFrequency (%)
8 9205
92.5%
0 629
 
6.3%
1 98
 
1.0%
9 17
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 9949
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
8 9205
92.5%
0 629
 
6.3%
1 98
 
1.0%
9 17
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
Common 9949
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
8 9205
92.5%
0 629
 
6.3%
1 98
 
1.0%
9 17
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9949
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
8 9205
92.5%
0 629
 
6.3%
1 98
 
1.0%
9 17
 
0.2%

qp53
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size77.9 KiB
8
9205 
0
 
635
1
 
83
9
 
26

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters9949
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row8
2nd row8
3rd row8
4th row8
5th row8

Common Values

ValueCountFrequency (%)
8 9205
92.5%
0 635
 
6.4%
1 83
 
0.8%
9 26
 
0.3%

Length

2023-03-23T10:34:49.314701image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-23T10:34:49.445360image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
8 9205
92.5%
0 635
 
6.4%
1 83
 
0.8%
9 26
 
0.3%

Most occurring characters

ValueCountFrequency (%)
8 9205
92.5%
0 635
 
6.4%
1 83
 
0.8%
9 26
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 9949
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
8 9205
92.5%
0 635
 
6.4%
1 83
 
0.8%
9 26
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
Common 9949
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
8 9205
92.5%
0 635
 
6.4%
1 83
 
0.8%
9 26
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9949
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
8 9205
92.5%
0 635
 
6.4%
1 83
 
0.8%
9 26
 
0.3%

qp54
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size77.9 KiB
8
9205 
1
 
409
0
 
326
9
 
9

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters9949
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row8
2nd row8
3rd row8
4th row8
5th row8

Common Values

ValueCountFrequency (%)
8 9205
92.5%
1 409
 
4.1%
0 326
 
3.3%
9 9
 
0.1%

Length

2023-03-23T10:34:49.586980image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-23T10:34:49.732589image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
8 9205
92.5%
1 409
 
4.1%
0 326
 
3.3%
9 9
 
0.1%

Most occurring characters

ValueCountFrequency (%)
8 9205
92.5%
1 409
 
4.1%
0 326
 
3.3%
9 9
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 9949
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
8 9205
92.5%
1 409
 
4.1%
0 326
 
3.3%
9 9
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 9949
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
8 9205
92.5%
1 409
 
4.1%
0 326
 
3.3%
9 9
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9949
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
8 9205
92.5%
1 409
 
4.1%
0 326
 
3.3%
9 9
 
0.1%

qp55
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size77.9 KiB
8
9205 
0
 
636
1
 
91
9
 
17

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters9949
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row8
2nd row8
3rd row8
4th row8
5th row8

Common Values

ValueCountFrequency (%)
8 9205
92.5%
0 636
 
6.4%
1 91
 
0.9%
9 17
 
0.2%

Length

2023-03-23T10:34:49.849273image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-23T10:34:49.982916image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
8 9205
92.5%
0 636
 
6.4%
1 91
 
0.9%
9 17
 
0.2%

Most occurring characters

ValueCountFrequency (%)
8 9205
92.5%
0 636
 
6.4%
1 91
 
0.9%
9 17
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 9949
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
8 9205
92.5%
0 636
 
6.4%
1 91
 
0.9%
9 17
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
Common 9949
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
8 9205
92.5%
0 636
 
6.4%
1 91
 
0.9%
9 17
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9949
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
8 9205
92.5%
0 636
 
6.4%
1 91
 
0.9%
9 17
 
0.2%

r1
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size77.9 KiB
0
8954 
1
995 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters9949
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 8954
90.0%
1 995
 
10.0%

Length

2023-03-23T10:34:50.097609image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-23T10:34:50.217326image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 8954
90.0%
1 995
 
10.0%

Most occurring characters

ValueCountFrequency (%)
0 8954
90.0%
1 995
 
10.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 9949
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 8954
90.0%
1 995
 
10.0%

Most occurring scripts

ValueCountFrequency (%)
Common 9949
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 8954
90.0%
1 995
 
10.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9949
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 8954
90.0%
1 995
 
10.0%

r2
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size77.9 KiB
0
7409 
1
1482 
8
995 
9
 
63

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters9949
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row8

Common Values

ValueCountFrequency (%)
0 7409
74.5%
1 1482
 
14.9%
8 995
 
10.0%
9 63
 
0.6%

Length

2023-03-23T10:34:50.321012image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-23T10:34:50.453661image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 7409
74.5%
1 1482
 
14.9%
8 995
 
10.0%
9 63
 
0.6%

Most occurring characters

ValueCountFrequency (%)
0 7409
74.5%
1 1482
 
14.9%
8 995
 
10.0%
9 63
 
0.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 9949
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 7409
74.5%
1 1482
 
14.9%
8 995
 
10.0%
9 63
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
Common 9949
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 7409
74.5%
1 1482
 
14.9%
8 995
 
10.0%
9 63
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9949
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 7409
74.5%
1 1482
 
14.9%
8 995
 
10.0%
9 63
 
0.6%

r3
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size77.9 KiB
0
6174 
1
2699 
8
995 
9
 
81

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters9949
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row9
4th row0
5th row8

Common Values

ValueCountFrequency (%)
0 6174
62.1%
1 2699
27.1%
8 995
 
10.0%
9 81
 
0.8%

Length

2023-03-23T10:34:50.580319image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-23T10:34:50.779785image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 6174
62.1%
1 2699
27.1%
8 995
 
10.0%
9 81
 
0.8%

Most occurring characters

ValueCountFrequency (%)
0 6174
62.1%
1 2699
27.1%
8 995
 
10.0%
9 81
 
0.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 9949
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 6174
62.1%
1 2699
27.1%
8 995
 
10.0%
9 81
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
Common 9949
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 6174
62.1%
1 2699
27.1%
8 995
 
10.0%
9 81
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9949
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 6174
62.1%
1 2699
27.1%
8 995
 
10.0%
9 81
 
0.8%

r4
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size77.9 KiB
0
6576 
1
2297 
8
995 
9
 
81

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters9949
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row1
4th row0
5th row8

Common Values

ValueCountFrequency (%)
0 6576
66.1%
1 2297
 
23.1%
8 995
 
10.0%
9 81
 
0.8%

Length

2023-03-23T10:34:51.068042image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-23T10:34:51.423070image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 6576
66.1%
1 2297
 
23.1%
8 995
 
10.0%
9 81
 
0.8%

Most occurring characters

ValueCountFrequency (%)
0 6576
66.1%
1 2297
 
23.1%
8 995
 
10.0%
9 81
 
0.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 9949
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 6576
66.1%
1 2297
 
23.1%
8 995
 
10.0%
9 81
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
Common 9949
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 6576
66.1%
1 2297
 
23.1%
8 995
 
10.0%
9 81
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9949
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 6576
66.1%
1 2297
 
23.1%
8 995
 
10.0%
9 81
 
0.8%

r5
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size77.9 KiB
1
7137 
0
1693 
8
995 
9
 
124

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters9949
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row1
4th row1
5th row8

Common Values

ValueCountFrequency (%)
1 7137
71.7%
0 1693
 
17.0%
8 995
 
10.0%
9 124
 
1.2%

Length

2023-03-23T10:34:51.695337image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-23T10:34:52.036430image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
1 7137
71.7%
0 1693
 
17.0%
8 995
 
10.0%
9 124
 
1.2%

Most occurring characters

ValueCountFrequency (%)
1 7137
71.7%
0 1693
 
17.0%
8 995
 
10.0%
9 124
 
1.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 9949
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 7137
71.7%
0 1693
 
17.0%
8 995
 
10.0%
9 124
 
1.2%

Most occurring scripts

ValueCountFrequency (%)
Common 9949
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 7137
71.7%
0 1693
 
17.0%
8 995
 
10.0%
9 124
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9949
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 7137
71.7%
0 1693
 
17.0%
8 995
 
10.0%
9 124
 
1.2%

r6
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size77.9 KiB
0
7046 
1
1807 
8
995 
9
 
101

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters9949
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row8

Common Values

ValueCountFrequency (%)
0 7046
70.8%
1 1807
 
18.2%
8 995
 
10.0%
9 101
 
1.0%

Length

2023-03-23T10:34:52.326655image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-23T10:34:52.650796image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 7046
70.8%
1 1807
 
18.2%
8 995
 
10.0%
9 101
 
1.0%

Most occurring characters

ValueCountFrequency (%)
0 7046
70.8%
1 1807
 
18.2%
8 995
 
10.0%
9 101
 
1.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 9949
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 7046
70.8%
1 1807
 
18.2%
8 995
 
10.0%
9 101
 
1.0%

Most occurring scripts

ValueCountFrequency (%)
Common 9949
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 7046
70.8%
1 1807
 
18.2%
8 995
 
10.0%
9 101
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9949
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 7046
70.8%
1 1807
 
18.2%
8 995
 
10.0%
9 101
 
1.0%

r7
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size77.9 KiB
1
6987 
0
1810 
8
995 
9
 
157

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters9949
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row1
4th row1
5th row8

Common Values

ValueCountFrequency (%)
1 6987
70.2%
0 1810
 
18.2%
8 995
 
10.0%
9 157
 
1.6%

Length

2023-03-23T10:34:52.920075image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-23T10:34:53.286084image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
1 6987
70.2%
0 1810
 
18.2%
8 995
 
10.0%
9 157
 
1.6%

Most occurring characters

ValueCountFrequency (%)
1 6987
70.2%
0 1810
 
18.2%
8 995
 
10.0%
9 157
 
1.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 9949
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 6987
70.2%
0 1810
 
18.2%
8 995
 
10.0%
9 157
 
1.6%

Most occurring scripts

ValueCountFrequency (%)
Common 9949
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 6987
70.2%
0 1810
 
18.2%
8 995
 
10.0%
9 157
 
1.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9949
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 6987
70.2%
0 1810
 
18.2%
8 995
 
10.0%
9 157
 
1.6%

r8
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size77.9 KiB
0
6941 
1
1902 
8
995 
9
 
111

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters9949
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row8

Common Values

ValueCountFrequency (%)
0 6941
69.8%
1 1902
 
19.1%
8 995
 
10.0%
9 111
 
1.1%

Length

2023-03-23T10:34:53.576311image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-23T10:34:53.789761image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 6941
69.8%
1 1902
 
19.1%
8 995
 
10.0%
9 111
 
1.1%

Most occurring characters

ValueCountFrequency (%)
0 6941
69.8%
1 1902
 
19.1%
8 995
 
10.0%
9 111
 
1.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 9949
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 6941
69.8%
1 1902
 
19.1%
8 995
 
10.0%
9 111
 
1.1%

Most occurring scripts

ValueCountFrequency (%)
Common 9949
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 6941
69.8%
1 1902
 
19.1%
8 995
 
10.0%
9 111
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9949
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 6941
69.8%
1 1902
 
19.1%
8 995
 
10.0%
9 111
 
1.1%

r9
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size77.9 KiB
0
6518 
1
2297 
8
995 
9
 
139

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters9949
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row8

Common Values

ValueCountFrequency (%)
0 6518
65.5%
1 2297
 
23.1%
8 995
 
10.0%
9 139
 
1.4%

Length

2023-03-23T10:34:53.902436image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-23T10:34:54.073990image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 6518
65.5%
1 2297
 
23.1%
8 995
 
10.0%
9 139
 
1.4%

Most occurring characters

ValueCountFrequency (%)
0 6518
65.5%
1 2297
 
23.1%
8 995
 
10.0%
9 139
 
1.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 9949
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 6518
65.5%
1 2297
 
23.1%
8 995
 
10.0%
9 139
 
1.4%

Most occurring scripts

ValueCountFrequency (%)
Common 9949
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 6518
65.5%
1 2297
 
23.1%
8 995
 
10.0%
9 139
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9949
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 6518
65.5%
1 2297
 
23.1%
8 995
 
10.0%
9 139
 
1.4%

Interactions

2023-03-23T10:34:06.753054image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-23T10:33:02.646622image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-23T10:33:05.564727image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-23T10:33:08.924741image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-23T10:33:12.408426image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-23T10:33:16.196306image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-23T10:33:18.610192image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-23T10:33:22.543384image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-23T10:33:26.067961image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-23T10:33:30.507092image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-23T10:33:33.556146image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-23T10:33:37.473677image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-23T10:33:41.839999image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-23T10:33:47.576672image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-23T10:33:51.977150image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-23T10:33:56.146004image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-23T10:33:59.632686image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-23T10:34:04.253736image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-23T10:34:07.087184image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-23T10:33:02.975967image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-23T10:33:05.947704image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-23T10:33:09.063369image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-23T10:33:12.548053image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-23T10:33:16.350887image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-23T10:33:18.987187image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-23T10:33:22.671041image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-23T10:33:26.260445image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-23T10:33:30.619792image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-23T10:33:34.095707image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-23T10:33:37.807783image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-23T10:33:41.977632image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
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2023-03-23T10:34:02.226170image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-23T10:34:05.815572image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-23T10:34:09.902637image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-23T10:33:04.639198image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-23T10:33:08.225609image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-23T10:33:11.749191image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-23T10:33:14.609547image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-23T10:33:17.810328image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-23T10:33:21.912249image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-23T10:33:25.413710image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-23T10:33:29.349190image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-23T10:33:32.184813image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-23T10:33:36.653864image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-23T10:33:41.061082image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-23T10:33:46.596284image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-23T10:33:51.083544image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-23T10:33:55.472804image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-23T10:33:58.300247image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-23T10:34:02.544341image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-23T10:34:05.939227image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-23T10:34:10.225799image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-23T10:33:04.777828image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-23T10:33:08.354284image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-23T10:33:11.871862image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-23T10:33:14.920716image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-23T10:33:17.919039image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-23T10:33:22.036918image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-23T10:33:25.542365image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-23T10:33:29.603509image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-23T10:33:32.290529image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-23T10:33:36.809447image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-23T10:33:41.208687image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-23T10:33:46.814713image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-23T10:33:51.398702image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-23T10:33:55.592484image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-23T10:33:58.585484image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-23T10:34:02.855472image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-23T10:34:06.074862image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-23T10:34:10.548908image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-23T10:33:04.916458image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-23T10:33:08.490931image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-23T10:33:12.005506image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-23T10:33:15.226897image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-23T10:33:18.032734image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-23T10:33:22.166570image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-23T10:33:25.667032image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-23T10:33:29.869797image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-23T10:33:32.551834image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-23T10:33:36.989973image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-23T10:33:41.354304image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-23T10:33:47.089965image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-23T10:33:51.543309image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-23T10:33:55.723135image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-23T10:33:58.886679image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-23T10:34:03.201549image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-23T10:34:06.197534image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-23T10:34:10.896977image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-23T10:33:05.068051image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-23T10:33:08.639504image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-23T10:33:12.147127image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-23T10:33:15.562999image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-23T10:33:18.164385image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-23T10:33:22.309188image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-23T10:33:25.808654image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-23T10:33:30.195926image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-23T10:33:32.892922image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-23T10:33:37.133582image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-23T10:33:41.513905image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-23T10:33:47.269485image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-23T10:33:51.694913image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-23T10:33:55.869745image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-23T10:33:59.224776image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-23T10:34:03.547621image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-23T10:34:06.339157image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-23T10:34:11.197174image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-23T10:33:05.203693image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-23T10:33:08.774143image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-23T10:33:12.269799image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-23T10:33:15.850231image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-23T10:33:18.292053image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-23T10:33:22.419715image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-23T10:33:25.928335image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-23T10:33:30.380431image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-23T10:33:33.140288image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-23T10:33:37.244286image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-23T10:33:41.652500image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-23T10:33:47.413100image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-23T10:33:51.829545image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-23T10:33:56.002389image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-23T10:33:59.475105image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-23T10:34:03.880729image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-23T10:34:06.456865image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Correlations

2023-03-23T10:34:54.441448image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
id2idade2idade4idade5idade6idade7idade8idade9idade10idade11idadeq3q4q13q14q16q17qp1regiaoq1q2q7q8q9q10q11q12q15q18q19q20q21qp2qp3qp4qp5qp6qp7qp8qp9qp10qp11qp12qp13qp14qp15qp16qp17qp18qp19qp20qp21qp22qp23qp24qp25qp26qp27qp28qp29qp30qp31qp32qp33qp34qp35qp36qp37qp38qp39qp40qp41qp42qp43qp44qp45qp46qp47qp48qp49qp50qp51qp52qp53qp54qp55r1r2r3r4r5r6r7r8r9
id21.0000.0760.076-0.008-0.048-0.065-0.3830.1850.466-0.0300.010-0.093-0.0610.018-0.011-0.0140.005-0.0070.8930.1120.1600.0790.0750.1480.1050.1210.1020.1120.1040.1170.1150.1440.0720.1170.0520.0400.0410.0490.0340.0440.0540.0260.0530.0560.0300.0440.0590.0400.0520.0540.0270.0630.0530.0180.0600.0520.0180.0370.0560.0180.0450.0550.0310.0380.0560.0070.0590.0560.0250.0420.0600.0350.0380.0590.0170.0390.0610.0180.0470.0570.0270.0380.0590.0550.0670.0490.0520.0910.1080.1160.0940.1020.1030.1070.117
idade20.0761.0000.2390.2400.1720.2210.0470.4700.4130.1360.3590.0530.055-0.0010.0090.078-0.004-0.0690.0660.0910.0940.0770.0570.0800.0730.0730.0750.0860.0500.0570.0810.0650.0470.0740.0520.0000.0470.0460.0000.0500.0510.0000.0650.0540.0000.0500.0610.0000.0670.0490.0000.0560.0520.0000.0370.0550.0000.0500.0590.0000.0610.0550.0000.0510.0580.0000.0830.0580.0000.0520.0650.0000.0700.0600.0000.0590.0590.0000.0570.0590.0000.0640.0610.0640.0510.0640.1200.0740.0740.0730.0720.0890.0780.0780.073
idade40.0760.2391.0000.2390.057-0.132-0.262-0.2010.2030.3370.070-0.0330.0120.0140.032-0.0040.009-0.0180.0870.0000.0460.0570.0550.0610.0740.0620.0750.0640.0710.0590.0430.0540.0000.0000.0100.0000.0120.0730.1140.0490.0460.0000.0790.0230.0000.0510.0470.0540.0000.0510.0760.0390.0350.0000.0430.0330.0600.0260.0460.0680.0190.0320.0360.0280.1120.0650.0430.0000.0300.0130.0000.0000.0000.0000.0000.0000.0400.0300.0420.0380.1070.0450.0000.0000.0360.0000.1020.0390.0400.0270.0590.0730.0760.0650.081
idade5-0.0080.2400.2391.0000.365-0.014-0.158-0.0570.3350.3930.071-0.013-0.011-0.0410.0230.007-0.030-0.0310.0550.1420.0690.0840.0640.0840.1110.0940.0890.0520.0370.0720.0600.0400.0540.1070.0550.0000.0940.0920.0000.1260.0810.0000.1650.0840.0000.1110.0920.1540.0540.1110.0000.0650.1260.0000.1510.0000.0000.1240.0770.1110.1070.0750.1180.0820.0360.0000.0980.0920.0720.0940.0670.0000.0620.0670.0000.0780.0820.0200.1020.1210.0720.1230.0840.0960.1410.1180.1130.0780.0000.0000.0060.0000.0380.0000.042
idade6-0.0480.1720.0570.3651.0000.5050.2890.290-0.221-0.3430.0380.069-0.039-0.0740.0090.039-0.074-0.0400.0410.1970.1770.1580.1260.1040.1350.2020.1970.1230.0870.0600.0570.0700.1720.2000.4030.6830.2340.3230.4820.2950.4180.6830.2730.1240.0000.1740.1400.0000.1940.2021.0000.2010.2691.0000.3810.1680.0000.2540.2010.0000.3810.1931.0000.1730.0800.0000.2040.2031.0000.2110.1510.0000.2110.1540.0000.2190.1851.0000.2010.2141.0000.0460.2190.1640.1610.3620.1180.1080.0290.0000.0000.0000.0000.0000.052
idade7-0.0650.221-0.132-0.0140.5051.0000.4830.517-0.200-0.260-0.0180.1290.1190.0340.0290.229-0.049-0.1920.2900.2570.2020.5140.2530.2220.2310.1950.2570.2280.3960.0300.1290.0230.3040.1500.2231.0000.1650.2231.0000.1910.2231.0000.0450.2231.0000.0450.1920.0000.1060.2361.0000.1060.2661.0000.2710.2231.0000.2090.2661.0000.2710.2231.0000.0000.1811.0000.1910.2361.0000.1060.1920.0000.1060.2321.0000.0000.2361.0000.1060.2361.0000.1060.1810.1810.2010.2660.2340.2470.1860.1430.2080.2280.2770.1890.302
idade8-0.3830.047-0.262-0.1580.2890.4831.0000.259-0.356-0.0520.1620.1110.0990.136-0.0150.2200.222-0.2330.3320.2980.2480.0000.1750.1160.0000.2790.2980.3120.2980.2200.0000.2680.1930.1170.0001.0000.0000.0001.0000.0000.0001.0000.0000.0001.0000.0000.0000.0000.0000.0001.0000.0000.0001.0000.1270.0001.0000.0820.0001.0000.1270.0001.0000.0000.0001.0000.0000.0001.0000.0000.0000.0000.0000.0001.0000.0000.0001.0000.0000.0001.0000.0000.0910.0910.0000.0000.0000.2590.0000.0790.0000.0000.0000.0810.000
idade90.1850.470-0.201-0.0570.2900.5170.2591.0000.885-0.1780.031-0.087-0.026-0.096-0.0320.055-0.240-0.1010.3000.1620.1600.0000.3640.1790.0000.0680.1620.1950.1620.3080.0970.1580.1700.0000.0001.0000.2130.0001.0000.1200.0001.0000.1200.0001.0000.1200.1700.0000.2990.1561.0000.2990.1561.0000.2990.0001.0000.2130.1561.0000.2990.0001.0000.0000.0001.0000.1200.1561.0000.2990.1700.0000.2990.0751.0000.0560.1561.0000.2990.1561.0000.2990.0000.0000.0000.1560.0000.0000.0000.0000.0000.0000.0000.0000.000
idade100.4660.4130.2030.335-0.221-0.200-0.3560.8851.000-0.0390.0020.1110.270-0.0080.347-0.325-0.051-0.0550.4490.2670.5190.0000.6730.3640.0000.6730.2670.4640.2670.4110.3510.0000.0000.0000.0001.0000.0000.0001.0000.0000.0001.0000.0000.0001.0000.0000.0000.0001.0000.0001.0001.0000.0001.0001.0000.0001.0000.0000.0001.0001.0000.0001.0000.0000.0001.0000.0000.0001.0001.0000.0000.0001.0000.0001.0001.0000.0001.0001.0000.0001.0001.0000.0000.0000.0000.0000.0000.0000.3070.0000.0000.0000.0000.0000.000
idade11-0.0300.1360.3370.393-0.343-0.260-0.052-0.178-0.0391.0000.134-0.443-0.429-0.373-0.501-0.536-0.3560.6330.8160.2810.3430.0000.6300.3890.0000.6300.2810.5820.2810.5610.3430.0510.0000.0000.0001.0000.0000.0001.0000.0000.0001.0000.0000.0001.0000.0000.0000.0001.0000.0001.0001.0000.0001.0001.0000.0001.0000.0000.0001.0001.0000.0001.0000.0000.0001.0000.0000.0001.0001.0000.0000.0001.0000.0001.0001.0000.0001.0001.0000.0001.0001.0000.0000.0000.0000.0000.0000.0000.4090.0000.0000.0000.0000.0000.000
idade0.0100.3590.0700.0710.038-0.0180.1620.0310.0020.1341.0000.1220.124-0.119-0.0490.161-0.127-0.0740.0100.1520.1510.1220.1000.1260.1050.1230.1300.1560.0890.0880.1380.0980.0990.1270.1230.0000.1450.1190.0360.1700.1180.0000.1690.1250.0110.1440.1440.0000.1810.1190.0000.1490.1250.0000.1560.1500.0000.1690.1530.0000.1640.1470.0000.1530.1330.0000.1900.1330.0000.1540.1580.0000.1760.1540.0030.1640.1390.0030.1590.1380.0000.1590.1590.1700.1100.1510.2460.1480.1490.1430.1430.1420.1430.1430.143
q3-0.0930.053-0.033-0.0130.0690.1290.111-0.0870.111-0.4430.1221.0000.7040.2090.2450.4720.218-0.5520.0861.0000.6910.5270.5520.5280.5710.5970.7160.4780.5590.5590.5450.5330.4990.7070.3790.1160.2370.3790.1160.2790.3790.1140.2730.3790.1200.2130.3790.1290.2680.3790.1170.2360.3790.1110.2370.3790.1120.1980.3790.1090.2130.3790.1120.2240.3790.1080.2660.3790.0990.2080.3790.1280.2070.3790.1020.2090.3790.1040.2180.3790.1140.1940.4380.4380.4380.4380.3580.4030.3810.3790.3280.3480.3120.3420.337
q4-0.0610.0550.012-0.011-0.0390.1190.099-0.0260.270-0.4290.1240.7041.0000.2400.2810.4670.264-0.5790.0781.0000.6870.5220.5470.5250.5620.5880.7090.4660.5510.5560.5390.5260.5000.7070.3800.1170.2370.3800.1170.2800.3800.1150.2730.3800.1210.2140.3800.1300.2690.3800.1180.2370.3800.1120.2370.3800.1130.1990.3800.1100.2130.3800.1130.2250.3800.1080.2670.3800.1000.2080.3800.1280.2080.3800.1030.2100.3800.1050.2190.3800.1150.1940.4390.4390.4390.4390.3560.3940.3690.3670.3170.3360.3000.3310.321
q130.018-0.0010.014-0.041-0.0740.0340.136-0.096-0.008-0.373-0.1190.2090.2401.0000.4730.1400.792-0.5320.0350.9380.6650.6650.6650.6650.6660.6660.7070.4620.6660.6650.6640.6650.6630.6630.5040.1100.2730.5040.1100.2630.5040.1090.2570.5040.1150.2460.5040.1230.2530.5040.1120.2720.5040.1060.2730.5040.1080.2290.5040.1050.2460.5040.1080.2590.5040.1030.2510.5040.1170.2400.5040.1210.2400.5040.1200.2420.5040.1000.2520.5040.1090.2240.5040.5040.5040.5040.3650.2580.2590.2580.2580.2580.2590.2580.258
q14-0.0110.0090.0320.0230.0090.029-0.015-0.0320.347-0.501-0.0490.2450.2810.4731.0000.2300.413-0.4830.0691.0000.6030.5170.5290.5320.5370.6010.7240.4840.5220.5250.5160.5100.5000.7070.3800.1170.2370.3800.1170.2800.3800.1150.2740.3800.1220.2140.3800.1300.2690.3800.1180.2370.3800.1120.2370.3800.1140.1990.3800.1110.2130.3800.1140.2250.3800.1090.2670.3800.1010.2090.3800.1290.2080.3800.1030.2100.3800.1060.2190.3800.1150.1950.4390.4390.4390.4390.3580.2380.2400.2420.2310.2360.2320.2330.234
q16-0.0140.078-0.0040.0070.0390.2290.2200.055-0.325-0.5360.1610.4720.4670.1400.2301.0000.188-0.5740.1041.0000.5920.5150.5060.5070.5080.6100.7080.5330.5140.5140.5100.5140.5000.7070.3800.1170.2370.3800.1170.2800.3800.1150.2730.3800.1210.2140.3800.1300.2690.3800.1180.2370.3800.1120.2370.3800.1130.1990.3800.1100.2130.3800.1130.2250.3800.1080.2670.3800.1000.2080.3800.1280.2080.3800.1030.2100.3800.1050.2190.3800.1150.1940.4390.4390.4390.4390.3560.2190.2200.2180.2150.2170.2180.2160.218
q170.005-0.0040.009-0.030-0.074-0.0490.222-0.240-0.051-0.356-0.1270.2180.2640.7920.4130.1881.000-0.5320.0350.9380.6650.6650.6650.6650.6660.6660.7070.4620.6660.6650.6640.6640.6630.6630.5040.1100.2730.5040.1100.2630.5040.1090.2570.5040.1150.2460.5040.1230.2530.5040.1120.2720.5040.1060.2730.5040.1080.2290.5040.1050.2460.5040.1080.2590.5040.1030.2510.5040.1170.2400.5040.1210.2400.5040.1200.2420.5040.1000.2520.5040.1090.2240.5040.5040.5040.5040.3650.2580.2590.2580.2590.2580.2600.2580.258
qp1-0.007-0.069-0.018-0.031-0.040-0.192-0.233-0.101-0.0550.633-0.074-0.552-0.579-0.532-0.483-0.574-0.5321.0000.0491.0000.5770.4990.4990.4990.4990.5770.7070.4470.4990.4990.4990.4990.7310.7260.4840.1450.3890.4770.1470.4610.4850.1470.4750.4730.1490.3700.4780.1550.4610.4710.1550.3940.4710.1330.3910.4720.1400.3510.4610.1380.3460.4700.1400.3690.4710.1300.4430.4760.1330.3610.4690.1620.3590.4650.1290.3600.4710.1240.3590.4630.1330.3390.5040.4990.4770.4990.4170.2430.2440.2430.2420.2420.2420.2430.242
regiao0.8930.0660.0870.0550.0410.2900.3320.3000.4490.8160.0100.0860.0780.0350.0690.1040.0350.0491.0000.0460.1310.0590.0710.1050.0820.0800.0700.0820.0480.0770.0820.1110.0430.0530.0270.0410.0250.0260.0390.0230.0270.0270.0270.0300.0390.0130.0360.0470.0240.0340.0340.0300.0280.0250.0270.0250.0280.0000.0250.0240.0200.0330.0350.0170.0280.0180.0300.0310.0350.0050.0350.0450.0110.0300.0260.0140.0280.0250.0210.0320.0390.0110.0270.0220.0360.0170.0300.0660.0890.0960.0740.0860.0790.0860.086
q10.1120.0910.0000.1420.1970.2570.2980.1620.2670.2810.1521.0001.0000.9381.0001.0000.9381.0000.0461.0001.0001.0001.0001.0001.0001.0001.0000.6331.0001.0001.0001.0001.0001.0000.7600.1670.4120.7600.1670.3960.7600.1650.3880.7600.1740.3720.7600.1860.3810.7600.1690.4110.7600.1610.4120.7600.1630.3460.7600.1580.3710.7600.1630.3910.7600.1560.3780.7600.1770.3630.7600.1840.3620.7600.1810.3650.7600.1510.3810.7600.1650.3380.7600.7600.7600.7600.3540.3540.3570.3540.3540.3550.3550.3540.354
q20.1600.0940.0460.0690.1770.2020.2480.1600.5190.3430.1510.6910.6870.6650.6030.5920.6650.5770.1311.0001.0000.6030.6180.6220.6230.6190.7130.4980.6120.6180.6340.6050.5770.7070.4390.1170.2380.4390.1170.2800.4390.1160.2740.4390.1220.2140.4390.1310.2690.4390.1190.2370.4390.1130.2380.4390.1140.1990.4390.1110.2140.4390.1140.2250.4390.1090.2670.4390.1010.2090.4390.1290.2080.4390.1040.2100.4390.1060.2190.4390.1160.1950.4390.4390.4390.4390.3580.3250.3110.3090.2770.2930.2660.2860.281
q70.0790.0770.0570.0840.1580.5140.0000.0000.0000.0000.1220.5270.5220.6650.5170.5150.6650.4990.0591.0000.6031.0000.5840.5910.6070.6020.7150.4810.5300.5350.5320.5170.5000.7070.3800.1170.2370.3800.1170.2800.3800.1150.2740.3800.1220.2140.3800.1300.2690.3800.1180.2370.3800.1120.2370.3800.1140.1990.3800.1110.2130.3800.1140.2250.3800.1090.2670.3800.1010.2090.3800.1290.2080.3800.1030.2100.3800.1060.2190.3800.1150.1950.4390.4390.4390.4390.3620.2360.2440.2420.2380.2430.2380.2400.240
q80.0750.0570.0550.0640.1260.2530.1750.3640.6730.6300.1000.5520.5470.6650.5290.5060.6650.4990.0711.0000.6180.5841.0000.6040.6270.5940.7180.4680.5360.5460.5370.5150.5000.7070.3800.1170.2370.3800.1170.2800.3800.1150.2740.3800.1220.2140.3800.1300.2690.3800.1180.2370.3800.1120.2370.3800.1140.1990.3800.1110.2130.3800.1140.2250.3800.1090.2670.3800.1010.2090.3800.1290.2080.3800.1030.2100.3800.1060.2190.3800.1150.1950.4390.4390.4390.4390.3640.2820.2790.2770.2670.2750.2590.2700.263
q90.1480.0800.0610.0840.1040.2220.1160.1790.3640.3890.1260.5280.5250.6650.5320.5070.6650.4990.1051.0000.6220.5910.6041.0000.5560.5930.7140.4720.5170.5260.5440.5120.5000.7070.3800.1170.2370.3800.1170.2800.3800.1150.2740.3800.1220.2140.3800.1300.2690.3800.1180.2370.3800.1120.2370.3800.1140.1990.3800.1110.2130.3800.1140.2250.3800.1090.2670.3800.1010.2090.3800.1290.2080.3800.1030.2100.3800.1060.2190.3800.1150.1950.4390.4390.4390.4390.3610.2490.2460.2440.2390.2460.2370.2440.241
q100.1050.0730.0740.1110.1350.2310.0000.0000.0000.0000.1050.5710.5620.6660.5370.5080.6660.4990.0821.0000.6230.6070.6270.5561.0000.5980.7290.4710.5610.5700.5440.5220.5000.7070.3800.1170.2370.3800.1170.2800.3800.1150.2740.3800.1220.2140.3800.1300.2690.3800.1180.2370.3800.1120.2370.3800.1140.1990.3800.1110.2130.3800.1140.2250.3800.1090.2670.3800.1010.2090.3800.1290.2080.3800.1030.2100.3800.1060.2190.3800.1150.1950.4390.4390.4390.4390.3670.2980.2900.2850.2760.2860.2590.2760.267
q110.1210.0730.0620.0940.2020.1950.2790.0680.6730.6300.1230.5970.5880.6660.6010.6100.6660.5770.0801.0000.6190.6020.5940.5930.5981.0000.7230.7070.5990.5950.5930.5850.5770.7070.4390.1170.2380.4390.1170.2800.4390.1160.2740.4390.1220.2140.4390.1310.2690.4390.1190.2370.4390.1130.2380.4390.1140.1990.4390.1110.2140.4390.1140.2250.4390.1090.2670.4390.1010.2090.4390.1290.2080.4390.1040.2100.4390.1060.2190.4390.1160.1950.4390.4390.4390.4390.3710.2280.2320.2320.2320.2340.2290.2350.237
q120.1020.0750.0750.0890.1970.2570.2980.1620.2670.2810.1300.7160.7090.7070.7240.7080.7070.7070.0701.0000.7130.7150.7180.7140.7290.7231.0000.4630.7260.7170.7130.7080.7070.7070.5370.1180.2910.5370.1180.2800.5370.1160.2740.5370.1220.2630.5370.1310.2690.5370.1190.2900.5370.1130.2910.5370.1150.2440.5370.1110.2620.5370.1150.2760.5370.1100.2670.5370.1250.2560.5370.1300.2550.5370.1280.2580.5370.1070.2690.5370.1160.2390.5380.5380.5380.5380.3660.2600.2610.2600.2610.2610.2620.2600.261
q150.1120.0860.0640.0520.1230.2280.3120.1950.4640.5820.1560.4780.4660.4620.4840.5330.4620.4470.0820.6330.4980.4810.4680.4720.4710.7070.4631.0000.4690.4700.4710.4680.4470.4480.3400.0740.1840.3400.0740.1770.3400.0730.1730.3400.0770.1660.3400.0820.1700.3400.0750.1830.3400.0710.1840.3400.0720.1540.3400.0700.1650.3400.0720.1740.3400.0690.1690.3400.0780.1620.3400.0810.1610.3400.0800.1630.3400.0670.1700.3400.0730.1510.3400.3400.3400.3400.3110.2340.2380.2400.2370.2370.2340.2390.241
q180.1040.0500.0710.0370.0870.3960.2980.1620.2670.2810.0890.5590.5510.6660.5220.5140.6660.4990.0481.0000.6120.5300.5360.5170.5610.5990.7260.4691.0000.6520.5500.5310.5000.7070.3800.1170.2370.3800.1170.2800.3800.1150.2740.3800.1220.2140.3800.1300.2690.3800.1180.2370.3800.1120.2370.3800.1140.1990.3800.1110.2130.3800.1140.2250.3800.1090.2670.3800.1010.2090.3800.1290.2080.3800.1030.2100.3800.1060.2190.3800.1150.1950.4390.4390.4390.4390.3560.2900.2860.2850.2710.2840.2530.2740.265
q190.1170.0570.0590.0720.0600.0300.2200.3080.4110.5610.0880.5590.5560.6650.5250.5140.6650.4990.0771.0000.6180.5350.5460.5260.5700.5950.7170.4700.6521.0000.5610.5430.5000.7070.3800.1170.2370.3800.1170.2800.3800.1150.2740.3800.1220.2140.3800.1300.2690.3800.1180.2370.3800.1120.2370.3800.1140.1990.3800.1110.2130.3800.1140.2250.3800.1090.2670.3800.1010.2090.3800.1290.2080.3800.1030.2100.3800.1060.2190.3800.1150.1950.4390.4390.4390.4390.3620.3010.3040.3070.2880.3070.2690.2990.283
q200.1150.0810.0430.0600.0570.1290.0000.0970.3510.3430.1380.5450.5390.6640.5160.5100.6640.4990.0821.0000.6340.5320.5370.5440.5440.5930.7130.4710.5500.5611.0000.5950.5000.7070.3800.1170.2370.3800.1170.2800.3800.1150.2740.3800.1220.2140.3800.1300.2690.3800.1180.2370.3800.1120.2370.3800.1140.1990.3800.1110.2130.3800.1140.2250.3800.1090.2670.3800.1010.2090.3800.1290.2080.3800.1030.2100.3800.1060.2190.3800.1150.1950.4390.4390.4390.4390.3600.2790.2720.2660.2540.2610.2460.2560.251
q210.1440.0650.0540.0400.0700.0230.2680.1580.0000.0510.0980.5330.5260.6650.5100.5140.6640.4990.1111.0000.6050.5170.5150.5120.5220.5850.7080.4680.5310.5430.5951.0000.5000.7070.3800.1170.2370.3800.1170.2800.3800.1150.2740.3800.1220.2140.3800.1300.2690.3800.1180.2370.3800.1120.2370.3800.1140.1990.3800.1110.2130.3800.1140.2250.3800.1090.2670.3800.1010.2090.3800.1290.2080.3800.1030.2100.3800.1060.2190.3800.1150.1950.4390.4390.4390.4390.3560.2570.2600.2540.2390.2460.2350.2430.238
qp20.0720.0470.0000.0540.1720.3040.1930.1700.0000.0000.0990.4990.5000.6630.5000.5000.6630.7310.0431.0000.5770.5000.5000.5000.5000.5770.7070.4470.5000.5000.5000.5001.0000.7200.4950.2550.3510.4860.2610.4150.4830.2540.4110.4770.2530.3200.4660.2530.3960.4720.2430.3430.4700.2190.3440.4550.2460.3010.4580.2640.2990.4570.2280.3200.4560.2020.3810.4600.1700.3160.4570.2240.3190.4600.1970.3140.4480.1930.3100.4560.2130.2980.4760.4770.4760.4750.3780.2200.2210.2200.2190.2190.2190.2190.220
qp30.1170.0740.0000.1070.2000.1500.1170.0000.0000.0000.1270.7070.7070.6630.7070.7070.6630.7260.0531.0000.7070.7070.7070.7070.7070.7070.7070.4480.7070.7070.7070.7070.7201.0000.7070.1550.3830.7070.1550.3690.7070.1530.3610.7070.1610.3460.7070.1730.3540.7070.1570.3820.7070.1490.3830.7070.1510.3220.7070.1470.3450.7070.1510.3630.7070.1450.3510.7070.1650.3370.7070.1710.3360.7070.1690.3390.7070.1410.3540.7070.1530.3140.7070.7070.7070.7070.4030.2850.2870.2850.2850.2850.2860.2850.285
qp40.0520.0520.0100.0550.4030.2230.0000.0000.0000.0000.1230.3790.3800.5040.3800.3800.5040.4840.0270.7600.4390.3800.3800.3800.3800.4390.5370.3400.3800.3800.3800.3800.4950.7071.0000.7070.5770.8270.5290.5690.7820.4610.5710.7340.4270.4380.7050.3960.5480.7090.4370.4490.7290.4200.4580.7030.4070.4180.6910.4360.4170.7080.4230.4410.6990.3940.5250.7000.3120.4370.6880.3640.4400.6850.3260.4550.6870.3460.4460.6950.3750.4230.6810.6640.7030.6760.4230.2510.2500.2560.2520.2540.2500.2530.251
qp50.0400.0000.0000.0000.6831.0001.0001.0001.0001.0000.0000.1160.1170.1100.1170.1170.1100.1450.0410.1670.1170.1170.1170.1170.1170.1170.1180.0740.1170.1170.1170.1170.2550.1550.7071.0000.0000.5330.6310.0160.4690.5390.0190.4250.4720.0120.3990.4220.0000.4250.4630.0000.4610.4630.0260.4290.4690.1160.4500.4830.0500.4280.4530.0150.4230.4140.0550.3750.4110.0150.3620.3560.1620.3920.3890.0000.3640.3530.1180.3810.3810.0240.1600.1640.1710.1610.0000.0000.0000.0000.0000.0000.0000.0000.000
qp60.0410.0470.0120.0940.2340.1650.0000.2130.0000.0000.1450.2370.2370.2730.2370.2370.2730.3890.0250.4120.2380.2370.2370.2370.2370.2380.2910.1840.2370.2370.2370.2370.3510.3830.5770.0001.0000.4740.0380.6680.4840.0140.6880.4650.0080.5300.4650.0370.6510.4450.0150.5250.4550.0470.5280.4670.0120.5050.4490.0000.4690.4520.0000.4860.4590.0370.6010.4720.0480.4970.4700.0200.4970.4820.0270.5040.4650.0380.4970.4710.0170.4950.4040.4180.3280.3900.3400.1960.1960.1960.1960.1960.1960.1960.196
qp70.0490.0460.0730.0920.3230.2230.0000.0000.0000.0000.1190.3790.3800.5040.3800.3800.5040.4770.0260.7600.4390.3800.3800.3800.3800.4390.5370.3400.3800.3800.3800.3800.4860.7070.8270.5330.4741.0000.7070.7070.8580.5520.6010.7610.4910.4450.7400.4900.5510.7430.5160.4470.7720.5100.4510.7470.4690.4310.7260.4850.4210.7380.4530.4460.7420.4490.5410.7110.3550.4270.7150.4110.4380.7050.3470.4490.7070.3840.4370.7240.4110.4260.6930.6640.7150.6810.4240.2560.2540.2610.2550.2570.2530.2560.254
qp80.0340.0000.1140.0000.4821.0001.0001.0001.0001.0000.0360.1160.1170.1100.1170.1170.1100.1470.0390.1670.1170.1170.1170.1170.1170.1170.1180.0740.1170.1170.1170.1170.2610.1550.5290.6310.0380.7071.0000.0000.5430.6740.0090.4800.5700.0860.4480.4900.0080.4870.5440.0200.5490.5450.0000.4660.5020.1550.4990.5050.0490.4650.4670.0270.4920.4870.0430.4280.4400.0430.4130.4150.2130.4180.4280.0000.4170.4090.1000.4300.4280.0380.1630.1620.1740.1650.0260.0140.0210.0210.0150.0160.0150.0210.017
qp90.0440.0500.0490.1260.2950.1910.0000.1200.0000.0000.1700.2790.2800.2630.2800.2800.2630.4610.0230.3960.2800.2800.2800.2800.2800.2800.2800.1770.2800.2800.2800.2800.4150.3690.5690.0160.6680.7070.0001.0000.6090.0200.7310.5680.0230.6480.5590.0820.6320.5240.0000.6220.5300.0190.6090.5640.0040.5900.5380.0000.5830.5510.0000.6040.5610.0240.6230.5530.0750.6020.5480.0250.6150.5560.0080.6200.5440.0370.6000.5700.0000.6150.4660.4790.3910.4580.3330.2350.2360.2360.2350.2350.2350.2350.235
qp100.0540.0510.0460.0810.4180.2230.0000.0000.0000.0000.1180.3790.3800.5040.3800.3800.5040.4850.0270.7600.4390.3800.3800.3800.3800.4390.5370.3400.3800.3800.3800.3800.4830.7070.7820.4690.4840.8580.5430.6091.0000.7070.7070.7750.4900.4710.7750.5000.5710.7480.5220.4510.7720.4850.4660.7450.4660.4300.7270.4680.4360.7250.4560.4510.7290.4180.5640.7300.3660.4470.7310.4170.4490.6960.3280.4610.7060.3920.4560.7150.3970.4400.6810.6630.6910.6690.4210.2530.2510.2560.2510.2540.2490.2520.250
qp110.0260.0000.0000.0000.6831.0001.0001.0001.0001.0000.0000.1140.1150.1090.1150.1150.1090.1470.0270.1650.1160.1150.1150.1150.1150.1160.1160.0730.1150.1150.1150.1150.2540.1530.4610.5390.0140.5520.6740.0200.7071.0000.0000.4740.5190.0860.4710.5180.0280.4950.5540.0440.5140.5150.0200.4520.4810.0310.4850.5050.0280.4570.4670.0280.4560.4500.0480.4440.4490.0300.4200.4220.0440.3990.4090.0000.4090.4060.0960.4060.3990.0070.1640.1600.1550.1590.0270.0160.0210.0210.0140.0180.0160.0230.020
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qp390.0420.0520.0130.0940.2110.1060.0000.2991.0001.0000.1540.2080.2080.2400.2090.2080.2400.3610.0050.3630.2090.2090.2090.2090.2090.2090.2560.1620.2090.2090.2090.2090.3160.3370.4370.0150.4970.4270.0430.6020.4470.0300.6240.4650.0930.5320.4770.0000.6970.4330.0350.5310.4360.0100.5370.4860.0000.5830.4880.0000.5840.4840.0000.5930.4880.0440.7330.5770.0001.0000.5210.0810.6290.5030.1100.5930.4900.0500.6020.4930.0000.5910.3640.3840.3000.3560.3600.2070.2070.2080.2070.2070.2070.2080.207
qp400.0600.0650.0000.0670.1510.1920.0000.1700.0000.0000.1580.3790.3800.5040.3800.3800.5040.4690.0350.7600.4390.3800.3800.3800.3800.4390.5370.3400.3800.3800.3800.3800.4570.7070.6880.3620.4700.7150.4130.5480.7310.4200.5700.7350.4480.4770.7700.4570.5960.7510.4380.4730.7670.4280.4860.7870.4290.4860.7990.4510.4930.8230.5090.5010.8050.4620.6060.8810.4560.5211.0000.7070.5770.8540.4200.5530.8150.4570.4990.8150.4780.4800.6890.6790.6580.6760.4420.2590.2590.2610.2590.2600.2580.2590.259
qp410.0350.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1280.1280.1210.1290.1280.1210.1620.0450.1840.1290.1290.1290.1290.1290.1290.1300.0810.1290.1290.1290.1290.2240.1710.3640.3560.0200.4110.4150.0250.4170.4220.0210.4540.4600.0410.4490.4640.0080.4240.4360.0360.4270.5000.0410.4210.4480.0170.4520.4630.0050.5100.5290.0230.4770.5610.0080.5590.6110.0810.7071.0000.0000.5140.5910.0000.4930.5370.0870.5090.5170.0020.1750.1800.1780.1830.0280.0150.0150.0200.0160.0170.0160.0160.019
qp420.0380.0700.0000.0620.2110.1060.0000.2991.0001.0000.1760.2070.2080.2400.2080.2080.2400.3590.0110.3620.2080.2080.2080.2080.2080.2080.2550.1610.2080.2080.2080.2080.3190.3360.4400.1620.4970.4380.2130.6150.4490.0440.6370.4780.2130.5410.4810.0290.6950.4330.0750.5410.4370.0000.5340.4810.0000.6940.4900.0420.5970.4780.0420.5740.4920.0470.7250.5160.0250.6290.5770.0001.0000.5400.1530.7090.4930.0530.6190.4900.0360.5970.3700.3730.2990.3610.3600.2080.2080.2080.2080.2080.2080.2080.208
qp430.0590.0600.0000.0670.1540.2320.0000.0750.0000.0000.1540.3790.3800.5040.3800.3800.5040.4650.0300.7600.4390.3800.3800.3800.3800.4390.5370.3400.3800.3800.3800.3800.4600.7070.6850.3920.4820.7050.4180.5560.6960.3990.5690.7140.4530.4770.7410.4380.6030.7030.4070.4750.7250.4230.4820.7500.4240.4860.7760.4380.4880.7850.4430.5050.7860.4690.5850.8130.3920.5030.8540.5140.5401.0000.5770.5770.8080.4460.5110.7850.4480.4820.6710.6710.6270.6570.4440.2610.2600.2620.2600.2610.2590.2610.260
qp440.0170.0000.0000.0000.0001.0001.0001.0001.0001.0000.0030.1020.1030.1200.1030.1030.1200.1290.0260.1810.1040.1030.1030.1030.1030.1040.1280.0800.1030.1030.1030.1030.1970.1690.3260.3890.0270.3470.4280.0080.3280.4090.0480.3780.4710.0000.3610.4710.0000.3370.4180.0000.3680.4650.0330.3630.4440.1080.3720.4680.0390.3790.4750.0740.4110.5300.0530.3920.4200.1100.4200.5910.1530.5771.0000.0000.4010.4970.0830.4080.4760.0240.1420.1420.1450.1460.0000.0000.0030.0000.0000.0000.0000.0000.000
qp450.0390.0590.0000.0780.2190.0000.0000.0561.0001.0000.1640.2090.2100.2420.2100.2100.2420.3600.0140.3650.2100.2100.2100.2100.2100.2100.2580.1630.2100.2100.2100.2100.3140.3390.4550.0000.5040.4490.0000.6200.4610.0000.6380.5010.0300.5400.5070.0000.7040.4570.0340.5390.4620.0100.5320.5020.0000.5570.5020.0000.6120.5080.0000.5780.4960.0490.7140.5220.0450.5930.5530.0000.7090.5770.0001.0000.5210.0550.6160.5110.0000.6050.4060.4010.3740.3970.3620.2090.2090.2090.2090.2090.2090.2090.209
qp460.0610.0590.0400.0820.1850.2360.0000.1560.0000.0000.1390.3790.3800.5040.3800.3800.5040.4710.0280.7600.4390.3800.3800.3800.3800.4390.5370.3400.3800.3800.3800.3800.4480.7070.6870.3640.4650.7070.4170.5440.7060.4090.5560.7250.4690.4620.7320.4350.5790.7350.4850.4600.7850.5510.4830.7770.5290.4680.7820.5020.4720.7890.4870.4830.8070.4950.5830.8290.4290.4900.8150.4930.4930.8080.4010.5211.0000.7070.5770.8470.5590.4850.7070.6650.6460.6790.4350.2550.2540.2570.2540.2560.2540.2550.254
qp470.0180.0000.0300.0201.0001.0001.0001.0001.0001.0000.0030.1040.1050.1000.1060.1050.1000.1240.0250.1510.1060.1060.1060.1060.1060.1060.1070.0670.1060.1060.1060.1060.1930.1410.3460.3530.0380.3840.4090.0370.3920.4060.0400.4480.4650.0000.4160.4270.0460.4570.4800.0230.5420.5970.0000.5140.5590.0120.4910.5230.0000.4750.4970.0000.4980.5910.0000.4720.5310.0500.4570.5370.0530.4460.4970.0550.7071.0000.0000.5330.5760.0610.1490.1490.1430.1480.0240.0100.0150.0150.0130.0170.0120.0150.012
qp480.0470.0570.0420.1020.2010.1060.0000.2991.0001.0000.1590.2180.2190.2520.2190.2190.2520.3590.0210.3810.2190.2190.2190.2190.2190.2190.2690.1700.2190.2190.2190.2190.3100.3540.4460.1180.4970.4370.1000.6000.4560.0960.6080.4600.0950.5080.4750.0870.6720.4290.0890.5190.4440.0000.5150.4740.0000.5460.4780.0910.5730.4760.0880.5670.4850.1040.7130.4960.1420.6020.4990.0870.6190.5110.0830.6160.5770.0001.0000.5060.1570.5850.3830.3720.3050.3540.3530.2030.2030.2040.2030.2030.2030.2030.203
qp490.0570.0590.0380.1210.2140.2360.0000.1560.0000.0000.1380.3790.3800.5040.3800.3800.5040.4630.0320.7600.4390.3800.3800.3800.3800.4390.5370.3400.3800.3800.3800.3800.4560.7070.6950.3810.4710.7240.4300.5700.7150.4060.5740.7300.4710.4700.7440.4660.5900.7450.4650.4870.7600.4750.4850.7720.4760.4810.7670.4950.4650.8000.5190.4870.8180.5150.5990.8310.4410.4930.8150.5090.4900.7850.4080.5110.8470.5330.5061.0000.7070.5770.6830.6640.6550.6710.4320.2540.2530.2550.2530.2540.2520.2540.253
qp500.0270.0000.1070.0721.0001.0001.0001.0001.0001.0000.0000.1140.1150.1090.1150.1150.1090.1330.0390.1650.1160.1150.1150.1150.1150.1160.1160.0730.1150.1150.1150.1150.2130.1530.3750.3810.0170.4110.4280.0000.3970.3990.0270.4580.4750.0040.4390.4820.0000.4430.4680.0440.4850.5020.0180.4780.4980.0430.4920.5230.0380.5140.5400.0000.5200.5230.0080.5210.5730.0000.4780.5170.0360.4480.4760.0000.5590.5760.1570.7071.0000.0000.1600.1630.1540.1570.0140.0020.0000.0000.0110.0000.0020.0000.003
qp510.0380.0640.0450.1230.0460.1060.0000.2991.0001.0000.1590.1940.1940.2240.1950.1940.2240.3390.0110.3380.1950.1950.1950.1950.1950.1950.2390.1510.1950.1950.1950.1950.2980.3140.4230.0240.4950.4260.0380.6150.4400.0070.6420.4450.0340.5180.4610.0000.6750.4220.0090.5290.4140.0000.5180.4700.0000.5420.4500.0000.5400.4600.0000.5640.4780.0000.7400.4850.0490.5910.4800.0020.5970.4820.0240.6050.4850.0610.5850.5770.0001.0000.3650.3770.2800.3450.3290.1900.1900.1900.1900.1900.1900.1900.190
qp520.0590.0610.0000.0840.2190.1810.0910.0000.0000.0000.1590.4380.4390.5040.4390.4390.5040.5040.0270.7600.4390.4390.4390.4390.4390.4390.5380.3400.4390.4390.4390.4390.4760.7070.6810.1600.4040.6930.1630.4660.6810.1640.4730.6740.1690.3790.6660.1780.4620.6650.1620.3930.6720.1590.4010.6840.1610.3750.6840.1530.3690.6900.1580.3820.6840.1510.4700.6790.1510.3640.6890.1750.3700.6710.1420.4060.7070.1490.3830.6830.1600.3651.0000.7690.6700.7160.4100.2390.2390.2410.2400.2400.2390.2400.239
qp530.0550.0640.0000.0960.1640.1810.0910.0000.0000.0000.1700.4380.4390.5040.4390.4390.5040.4990.0220.7600.4390.4390.4390.4390.4390.4390.5380.3400.4390.4390.4390.4390.4770.7070.6640.1640.4180.6640.1620.4790.6630.1600.4890.6660.1690.3940.6680.1780.4720.6560.1640.4020.6570.1560.4030.7010.1570.3720.6700.1530.3760.6720.1570.3910.6550.1530.4590.6630.1530.3840.6790.1800.3730.6710.1420.4010.6650.1490.3720.6640.1630.3770.7691.0000.6550.6980.4110.2390.2390.2400.2390.2400.2400.2390.239
qp540.0670.0510.0360.1410.1610.2010.0000.0000.0000.0000.1100.4380.4390.5040.4390.4390.5040.4770.0360.7600.4390.4390.4390.4390.4390.4390.5380.3400.4390.4390.4390.4390.4760.7070.7030.1710.3280.7150.1740.3910.6910.1550.3810.6670.1740.2990.6580.1740.3780.6750.1590.3340.6880.1520.3350.6670.1650.2860.6390.1490.2980.6570.1540.3140.6510.1470.3680.6420.1390.3000.6580.1780.2990.6270.1450.3740.6460.1430.3050.6550.1540.2800.6700.6551.0000.7160.3940.2320.2310.2360.2330.2340.2320.2340.232
qp550.0490.0640.0000.1180.3620.2660.0000.1560.0000.0000.1510.4380.4390.5040.4390.4390.5040.4990.0170.7600.4390.4390.4390.4390.4390.4390.5380.3400.4390.4390.4390.4390.4750.7070.6760.1610.3900.6810.1650.4580.6690.1590.4520.6720.1690.3510.6680.1810.4390.6690.1660.3770.6760.1620.3730.6830.1610.3520.6570.1560.3490.6640.1600.3590.6640.1630.4300.6670.1430.3560.6760.1830.3610.6570.1460.3970.6790.1480.3540.6710.1570.3450.7160.6980.7161.0000.4080.2380.2380.2400.2380.2390.2370.2390.238
r10.0520.1200.1020.1130.1180.2340.0000.0000.0000.0000.2460.3580.3560.3650.3580.3560.3650.4170.0300.3540.3580.3620.3640.3610.3670.3710.3660.3110.3560.3620.3600.3560.3780.4030.4230.0000.3400.4240.0260.3330.4210.0270.3280.4230.0280.3270.4360.0060.3510.4160.0000.3190.4170.0090.3270.4380.0000.3410.4410.0000.3580.4370.0190.3660.4290.0150.3410.4410.0310.3600.4420.0280.3600.4440.0000.3620.4350.0240.3530.4320.0140.3290.4100.4110.3940.4081.0001.0001.0001.0001.0001.0001.0001.0001.000
r20.0910.0740.0390.0780.1080.2470.2590.0000.0000.0000.1480.4030.3940.2580.2380.2190.2580.2430.0660.3540.3250.2360.2820.2490.2980.2280.2600.2340.2900.3010.2790.2570.2200.2850.2510.0000.1960.2560.0140.2350.2530.0160.2320.2510.0150.1880.2560.0000.2480.2450.0000.1840.2470.0000.1890.2580.0000.1970.2580.0000.2070.2570.0040.2110.2520.0000.2400.2590.0190.2070.2590.0150.2080.2610.0000.2090.2550.0100.2030.2540.0020.1900.2390.2390.2320.2381.0001.0000.7580.7160.6900.7120.6630.7300.676
r30.1080.0740.0400.0000.0290.1860.0000.0000.3070.4090.1490.3810.3690.2590.2400.2200.2590.2440.0890.3570.3110.2440.2790.2460.2900.2320.2610.2380.2860.3040.2720.2600.2210.2870.2500.0000.1960.2540.0210.2360.2510.0210.2320.2500.0160.1890.2560.0000.2480.2450.0000.1840.2460.0090.1890.2570.0000.1970.2580.0060.2070.2560.0140.2110.2510.0030.2410.2590.0190.2070.2590.0150.2080.2600.0030.2090.2540.0150.2030.2530.0000.1900.2390.2390.2310.2381.0000.7581.0000.7570.6910.7070.6670.7130.702
r40.1160.0730.0270.0000.0000.1430.0790.0000.0000.0000.1430.3790.3670.2580.2420.2180.2580.2430.0960.3540.3090.2420.2770.2440.2850.2320.2600.2400.2850.3070.2660.2540.2200.2850.2560.0000.1960.2610.0210.2360.2560.0210.2320.2540.0180.1890.2580.0000.2480.2480.0000.1840.2490.0080.1890.2600.0000.1970.2590.0000.2070.2580.0110.2110.2540.0030.2410.2610.0170.2080.2610.0200.2080.2620.0000.2090.2570.0150.2040.2550.0000.1900.2410.2400.2360.2401.0000.7160.7571.0000.6990.7260.6790.7280.698
r50.0940.0720.0590.0060.0000.2080.0000.0000.0000.0000.1430.3280.3170.2580.2310.2150.2590.2420.0740.3540.2770.2380.2670.2390.2760.2320.2610.2370.2710.2880.2540.2390.2190.2850.2520.0000.1960.2550.0150.2350.2510.0140.2310.2500.0180.1880.2550.0000.2480.2450.0000.1840.2460.0000.1890.2570.0000.1960.2580.0000.2070.2560.0120.2110.2510.0040.2400.2590.0190.2070.2590.0160.2080.2600.0000.2090.2540.0130.2030.2530.0110.1900.2400.2390.2330.2381.0000.6900.6910.6991.0000.7340.7560.7470.687
r60.1020.0890.0730.0000.0000.2280.0000.0000.0000.0000.1420.3480.3360.2580.2360.2170.2580.2420.0860.3550.2930.2430.2750.2460.2860.2340.2610.2370.2840.3070.2610.2460.2190.2850.2540.0000.1960.2570.0160.2350.2540.0180.2310.2520.0180.1880.2570.0080.2480.2460.0000.1840.2480.0120.1890.2580.0000.1960.2590.0000.2070.2570.0100.2110.2530.0080.2400.2600.0170.2070.2600.0170.2080.2610.0000.2090.2560.0170.2030.2540.0000.1900.2400.2400.2340.2391.0000.7120.7070.7260.7341.0000.7290.8090.733
r70.1030.0780.0760.0380.0000.2770.0000.0000.0000.0000.1430.3120.3000.2590.2320.2180.2600.2420.0790.3550.2660.2380.2590.2370.2590.2290.2620.2340.2530.2690.2460.2350.2190.2860.2500.0000.1960.2530.0150.2350.2490.0160.2310.2490.0160.1880.2540.0000.2480.2440.0000.1840.2450.0000.1890.2560.0000.1960.2570.0000.2070.2550.0110.2130.2510.0080.2400.2580.0180.2070.2580.0160.2080.2590.0000.2090.2540.0120.2030.2520.0020.1900.2390.2400.2320.2371.0000.6630.6670.6790.7560.7291.0000.7410.698
r80.1070.0780.0650.0000.0000.1890.0810.0000.0000.0000.1430.3420.3310.2580.2330.2160.2580.2430.0860.3540.2860.2400.2700.2440.2760.2350.2600.2390.2740.2990.2560.2430.2190.2850.2530.0000.1960.2560.0210.2350.2520.0230.2310.2510.0190.1880.2560.0000.2480.2450.0000.1840.2470.0090.1890.2580.0000.1970.2580.0000.2070.2570.0070.2110.2520.0050.2410.2590.0180.2080.2590.0160.2080.2610.0000.2090.2550.0150.2030.2540.0000.1900.2400.2390.2340.2391.0000.7300.7130.7280.7470.8090.7411.0000.762
r90.1170.0730.0810.0420.0520.3020.0000.0000.0000.0000.1430.3370.3210.2580.2340.2180.2580.2420.0860.3540.2810.2400.2630.2410.2670.2370.2610.2410.2650.2830.2510.2380.2200.2850.2510.0000.1960.2540.0170.2350.2500.0200.2310.2500.0160.1880.2550.0000.2480.2440.0000.1840.2460.0000.1890.2570.0000.1970.2570.0000.2070.2560.0040.2110.2510.0050.2410.2580.0150.2070.2590.0190.2080.2600.0000.2090.2540.0120.2030.2530.0030.1900.2390.2390.2320.2381.0000.6760.7020.6980.6870.7330.6980.7621.000

Missing values

2023-03-23T10:34:11.956141image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-03-23T10:34:12.672227image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-03-23T10:34:13.909706image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

id2regiaoidade2idade4idade5idade6idade7idade8idade9idade10idade11idade12idadeq1q2q3q4q7q8q9q10q11q12q13q14q15q16q17q18q19q20q21qp1qp2qp3qp4qp5qp6qp7qp8qp9qp10qp11qp12qp13qp14qp15qp16qp17qp18qp19qp20qp21qp22qp23qp24qp25qp26qp27qp28qp29qp30qp31qp32qp33qp34qp35qp36qp37qp38qp39qp40qp41qp42qp43qp44qp45qp46qp47qp48qp49qp50qp51qp52qp53qp54qp55r1r2r3r4r5r6r7r8r9
020200001168.0NaNNaNNaNNaNNaNNaNNaNNaNNaN60113211110051500511118888888888888888888888888888888888888888888888888888888011101011
120200002136.0NaNNaNNaNNaNNaNNaNNaNNaNNaN62113311110061200411198888888888888888888888888888888888888888888888888888888001010100
220200003178.0NaNNaNNaNNaNNaNNaNNaNNaNNaN7810531091100580022228888888888888888888888888888888888888888888888888888888009110100
320200004178.0NaNNaNNaNNaNNaNNaNNaNNaNNaN78113211110021510112118888888888888888888888888888888888888888888888888888888000010100
420200005159.0NaNNaNNaNNaNNaNNaNNaNNaNNaN891053999922888808812998888888888888888888888888888888888888888888888888888888188888888
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